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    <id>5879</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber>528</articleNumber>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-04-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology</title>
    <abstract language="eng">Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems.</abstract>
    <parentTitle language="eng">CHI'25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems</parentTitle>
    <identifier type="isbn">979-8-4007-1394-1</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-58797</identifier>
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    <enrichment key="conference_title">CHI 2025: CHI Conference on Human Factors in Computing Systems</enrichment>
    <enrichment key="conference_place">Yokohama Japan</enrichment>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3706598.3713319</enrichment>
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    <enrichment key="THI_conferenceName">CHI 2025: CHI Conference on Human Factors in Computing Systems, Yokohama (Japan), 26.04. - 01.05.2025</enrichment>
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    <licence>Creative Commons BY-NC-SA 4.0</licence>
    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
    </author>
    <editor>
      <first_name>Naomi</first_name>
      <last_name>Yamashita</last_name>
    </editor>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <editor>
      <first_name>Vanessa</first_name>
      <last_name>Evers</last_name>
    </editor>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Krügel</last_name>
    </author>
    <editor>
      <first_name>Koji</first_name>
      <last_name>Yatani</last_name>
    </editor>
    <author>
      <first_name>Angelika</first_name>
      <last_name>Kießig</last_name>
    </author>
    <editor>
      <first_name>Xianghua</first_name>
      <last_name>Ding</last_name>
    </editor>
    <author>
      <first_name>Alexis</first_name>
      <last_name>Fritz</last_name>
    </author>
    <editor>
      <first_name>Bongshin</first_name>
      <last_name>Lee</last_name>
    </editor>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <editor>
      <first_name>Marshini</first_name>
      <last_name>Chetty</last_name>
    </editor>
    <editor>
      <first_name>Phoebe</first_name>
      <last_name>Toups-Dugas</last_name>
    </editor>
    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
    </author>
    <author>
      <first_name>Maximilian C.</first_name>
      <last_name>Köller</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
    </author>
    <author>
      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
    </author>
    <author>
      <first_name>Georgios</first_name>
      <last_name>Paraschou</last_name>
    </author>
    <author>
      <first_name>Mun Keong</first_name>
      <last_name>Kok</last_name>
    </author>
    <author>
      <first_name>Alexander F. H.</first_name>
      <last_name>Haake</last_name>
    </author>
    <author>
      <first_name>Ronald R.</first_name>
      <last_name>de Krijger</last_name>
    </author>
    <author>
      <first_name>Andreas F.-P.</first_name>
      <last_name>Sonnen</last_name>
    </author>
    <author>
      <first_name>Tanit</first_name>
      <last_name>Kasantikul</last_name>
    </author>
    <author>
      <first_name>Gerry M.</first_name>
      <last_name>Dorrestein</last_name>
    </author>
    <author>
      <first_name>Rebecca C.</first_name>
      <last_name>Smedley</last_name>
    </author>
    <author>
      <first_name>Nikolas</first_name>
      <last_name>Stathonikos</last_name>
    </author>
    <author>
      <first_name>Matthias</first_name>
      <last_name>Uhl</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5879/3706598.3713319-1.pdf</file>
  </doc>
  <doc>
    <id>3088</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>3</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-02-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Classification of visibility in multi-stain microscopy images</title>
    <parentTitle language="eng">Medical Imaging with Deep Learning: MIDL 2022 Short Papers</parentTitle>
    <identifier type="url">https://openreview.net/forum?id=-GsA-mUVmm</identifier>
    <enrichment key="THI_review">editorial review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">Medical Imaging with Deep Learning (MIDL 2022), Zurich (Switzerland), 06.-08.07.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_DownloadUrl">https://2022.midl.io/papers/a_s_7</enrichment>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Samir</first_name>
      <last_name>Jabari</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic figure classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>dual input networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>attention</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>immunohistochemical staining</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
  </doc>
  <doc>
    <id>1314</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>211</pageFirst>
    <pageLast>226</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>59</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy</title>
    <abstract language="eng">The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13141</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1177/03009858211067478</enrichment>
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    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY-NC 4.0</licence>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
    </author>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Charles-Antoine</first_name>
      <last_name>Assenmacher</last_name>
    </author>
    <author>
      <first_name>Kathrin</first_name>
      <last_name>Becker</last_name>
    </author>
    <author>
      <first_name>Mark</first_name>
      <last_name>Bennett</last_name>
    </author>
    <author>
      <first_name>Sarah M.</first_name>
      <last_name>Corner</last_name>
    </author>
    <author>
      <first_name>Brieuc</first_name>
      <last_name>Cossic</last_name>
    </author>
    <author>
      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
    </author>
    <author>
      <first_name>Martina</first_name>
      <last_name>Dettwiler</last_name>
    </author>
    <author>
      <first_name>Beatriz</first_name>
      <last_name>Garcia Gonzalez</last_name>
    </author>
    <author>
      <first_name>Corinne</first_name>
      <last_name>Gurtner</last_name>
    </author>
    <author>
      <first_name>Ann-Kathrin</first_name>
      <last_name>Haverkamp</last_name>
    </author>
    <author>
      <first_name>Annabelle</first_name>
      <last_name>Heier</last_name>
    </author>
    <author>
      <first_name>Annika</first_name>
      <last_name>Lehmbecker</last_name>
    </author>
    <author>
      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
    </author>
    <author>
      <first_name>Erica L.</first_name>
      <last_name>Noland</last_name>
    </author>
    <author>
      <first_name>Stephanie</first_name>
      <last_name>Plog</last_name>
    </author>
    <author>
      <first_name>Anja</first_name>
      <last_name>Schmidt</last_name>
    </author>
    <author>
      <first_name>Franziska</first_name>
      <last_name>Sebastian</last_name>
    </author>
    <author>
      <first_name>Dodd G.</first_name>
      <last_name>Sledge</last_name>
    </author>
    <author>
      <first_name>Rebecca C.</first_name>
      <last_name>Smedley</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
    </author>
    <author>
      <first_name>Tuddow</first_name>
      <last_name>Thaiwong</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Fuchs-Baumgartinger</last_name>
    </author>
    <author>
      <first_name>Donald J.</first_name>
      <last_name>Meuten</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>canine cutaneous mast cell tumors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic figures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic count</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated image analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>computer assistance</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1314/03009858211067478.pdf</file>
  </doc>
  <doc>
    <id>1185</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>243</pageFirst>
    <pageLast>257</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>58</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mitotic Figures - Normal, Atypical, and Imposters: A Guide to Identification</title>
    <parentTitle language="eng">Veterinary pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1177/0300985820980049</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Frances M.</first_name>
      <last_name>Moore</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Luong</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Harold</first_name>
      <last_name>Tvedten</last_name>
    </author>
    <author>
      <first_name>Elisa N.</first_name>
      <last_name>Salas</last_name>
    </author>
    <author>
      <first_name>Derick</first_name>
      <last_name>Whitley</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Donald J.</first_name>
      <last_name>Meuten</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic figure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic count</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MC</value>
    </subject>
    <subject>
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    <title language="eng">Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing</title>
    <abstract language="eng">AbstractHistopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.</abstract>
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    <title language="eng">Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology</title>
    <parentTitle language="eng">2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)</parentTitle>
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      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
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      <first_name>Marco</first_name>
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      <first_name>Christof</first_name>
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      <first_name>Nikolas</first_name>
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      <first_name>Mathias</first_name>
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      <first_name>Jingna</first_name>
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      <first_name>Robert</first_name>
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      <first_name>Andreas</first_name>
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      <value>Histopathology</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Domain Shift</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Representation Learning</value>
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    <subject>
      <language>eng</language>
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      <value>Barlow Twins</value>
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    <title language="eng">Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge</title>
    <abstract language="eng">Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today’s deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking.</abstract>
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    <title language="eng">Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&amp;E slide analysis</title>
    <abstract language="eng">The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&amp;E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&amp;E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&amp;E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&amp;E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&amp;E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&amp;E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&amp;E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&amp;E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.</abstract>
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However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms\u2019 performance. Unlike H&amp;amp;E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&amp;amp;E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&amp;amp;E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&amp;amp;E-related dataset, impacting model performance. 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    <title language="eng">Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry</title>
    <abstract language="eng">Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning–based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701–0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737–0.991) and 0.943 (95% CI = 0.889–0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
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The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning\u2013based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. 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    <author>
      <first_name>Andreas</first_name>
      <last_name>Haghofer</last_name>
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      <first_name>Eda</first_name>
      <last_name>Parlak</last_name>
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      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
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      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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      <first_name>Charles-Antoine</first_name>
      <last_name>Assenmacher</last_name>
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      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
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      <first_name>Andrea</first_name>
      <last_name>Fuchs-Baumgartinger</last_name>
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      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
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      <first_name>Kathrin</first_name>
      <last_name>Jäger</last_name>
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      <first_name>Robert</first_name>
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      <first_name>Sophie</first_name>
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      <first_name>Barbara</first_name>
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      <first_name>Hannah</first_name>
      <last_name>Janout</last_name>
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      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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      <first_name>Josef</first_name>
      <last_name>Scharinger</last_name>
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      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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      <first_name>Stephan M.</first_name>
      <last_name>Winkler</last_name>
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      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <language>eng</language>
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    <pageNumber>8</pageNumber>
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    <issue>6</issue>
    <volume>10</volume>
    <articleNumber>e70008</articleNumber>
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    <publisherName>Wiley</publisherName>
    <publisherPlace>Chichester</publisherPlace>
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    <title language="eng">Breast cancer survival prediction using an automated mitosis detection pipeline</title>
    <abstract language="eng">AbstractMitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter‐ and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)‐supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long‐term follow‐up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI‐based MC and light‐microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni‐ and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light‐microscopic MC.</abstract>
    <parentTitle language="eng">The Journal of Pathology: Clinical Research</parentTitle>
    <identifier type="issn">2056-4538</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-53087</identifier>
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It is, however, subject to inter\u2010 and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)\u2010supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2\u2009mm&lt;jats:sup&gt;2&lt;\/jats:sup&gt;. 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In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light\u2010microscopic MC.&lt;\/jats:p&gt;","DOI":"10.1002\/2056-4538.70008","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T14:39:46Z","timestamp":1730126386000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Breast cancer survival prediction using an automated mitosis detection pipeline"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5457-7580","authenticated-orcid":false,"given":"Nikolas","family":"Stathonikos","sequence":"first","affiliation":[{"name":"Pathology University Medical Centre Utrecht  Utrecht The Netherlands"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5294-5247","authenticated-orcid":false,"given":"Marc","family":"Aubreville","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt  Ingolstadt Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5306-6797","authenticated-orcid":false,"given":"Sjoerd","family":"de Vries","sequence":"additional","affiliation":[{"name":"Digital Health University Medical Centre Utrecht  Utrecht The Netherlands"},{"name":"Information and Computing Sciences Utrecht University  Utrecht The Netherlands"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9065-0554","authenticated-orcid":false,"given":"Frauke","family":"Wilm","sequence":"additional","affiliation":[{"name":"Pattern Recognition Lab Friedrich\u2010Alexander\u2010Universit\u00e4t (FAU) Erlangen\u2010N\u00fcrnberg  Erlangen Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2402-9997","authenticated-orcid":false,"given":"Christof A","family":"Bertram","sequence":"additional","affiliation":[{"name":"Institute of Pathology University of Veterinary Medicine Vienna  Vienna Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1711-3098","authenticated-orcid":false,"given":"Mitko","family":"Veta","sequence":"additional","affiliation":[{"name":"Pathology University Medical Centre Utrecht  Utrecht The Netherlands"},{"name":"Medical Image Analysis Group TU Eindhoven  Eindhoven The Netherlands"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0658-2745","authenticated-orcid":false,"given":"Paul J","family":"van Diest","sequence":"additional","affiliation":[{"name":"Pathology University Medical Centre Utrecht  Utrecht The Netherlands"}]}],"member":"311","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray F","year":"2018","journal-title":"CA Cancer J Clin"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s00428-021-03141-2","article-title":"Grading of invasive breast carcinoma: the way forward","volume":"480","author":"Dooijeweert C","year":"2022","journal-title":"Virchows Arch"},{"key":"e_1_2_7_4_1","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1186\/bcr2607","article-title":"Breast cancer prognostic classification in the molecular era: the role of histological grade","volume":"12","author":"Rakha EA","year":"2010","journal-title":"Breast Cancer Res"},{"key":"e_1_2_7_5_1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1111\/j.1365-2559.1991.tb00229.x","article-title":"Pathological prognostic factors in breast cancer. 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      <first_name>Nikolas</first_name>
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      <first_name>Sjoerd</first_name>
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      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
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      <first_name>Christof</first_name>
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      <first_name>Mitko</first_name>
      <last_name>Veta</last_name>
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      <first_name>Paul J</first_name>
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    <title language="eng">Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility</title>
    <abstract language="eng">The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.</abstract>
    <parentTitle language="eng">Veterinary Sciences</parentTitle>
    <identifier type="issn">2306-7381</identifier>
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We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and\/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists\u2019 NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists\u2019 estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. 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Am. Anim. Hosp. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1177\/030098588101800409","article-title":"Classification of Lung Carcinomas in the Dog and Cat","volume":"18","author":"Moulton","year":"1981","journal-title":"Vet. Pathol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1177\/030098589603300601","article-title":"Primary Lung Neoplasia in a Beagle Colony","volume":"33","author":"Hahn","year":"1996","journal-title":"Vet. Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Able, H., Wolf-Ringwall, A., Rendahl, A., Ober, C.P., Seelig, D.M., Wilke, C.T., and Lawrence, J. (2021). Computed Tomography Radiomic Features Hold Prognostic Utility for Canine Lung Tumors: An Analytical Study. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0256139"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"327","DOI":"10.5326\/0380327","article-title":"Correlation between Fine-Needle Aspiration Cytopathology and Histopathology of the Lung in Dogs and Cats","volume":"38","author":"Deberry","year":"2002","journal-title":"J. Am. Anim. Hosp. Assoc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1093\/carcin\/19.6.959","article-title":"Rapid Detection of K-Ras Gene Mutations in Canine Lung Cancer Using Single-Strand Conformational Polymorphism Analysis","volume":"19","author":"Griffey","year":"1998","journal-title":"Carcinogenesis"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.2460\/javma.1997.211.11.1422","article-title":"Evaluation of Prognostic Factors for Dogs with Primary Lung Tumors: 67 cases (1985\u20131992)","volume":"211","author":"McNiel","year":"1997","journal-title":"J. Am. Vet. Med Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1055\/a-1949-0125","article-title":"Klinisch-Pathologische Befunde, Postoperative \u00dcberlebenszeiten Und Prognostische Faktoren Bei Hunden Mit Prim\u00e4ren Lungenkarzinomen","volume":"50","author":"Plavec","year":"2022","journal-title":"Tier\u00e4rztliche Prax. Ausg. Kleintiere Heimtiere"},{"key":"ref_9","first-page":"106","article-title":"Classification of Primary Lung Tumors in Dogs: 210 Cases (1975\u20131985)","volume":"195","author":"Ogilvie","year":"1989","journal-title":"J. Am. Vet. Med. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/j.1748-5827.2007.00403.x","article-title":"Impact of Primary Tumour Stage on Survival in Dogs with Solitary Lung Tumours","volume":"49","author":"Polton","year":"2008","journal-title":"J. Small Anim. Pract."},{"key":"ref_11","first-page":"109","article-title":"Prognostic Factors for Tumor Remission and Survival in Dogs after Surgery for Primary Lung Tumor: 76 Cases (1975\u20131985)","volume":"195","author":"Ogilvie","year":"1989","journal-title":"J. Am. Vet. Med. Assoc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kessler, M. (2022). Kleintieronkologie, Thieme. [4th ed.].","DOI":"10.1055\/b-006-163261"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Meuten, D.J. (2016). Tumors in Domestic Animals, Wiley-Blackwell. [5th ed.]. Tumors of the Respiratory Tract.","DOI":"10.1002\/9781119181200"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1111\/vco.12582","article-title":"Retrospective Evaluation of a Modified Human Lung Cancer Stage Classification in Dogs with Surgically Excised Primary Pulmonary Carcinomas","volume":"18","author":"Lee","year":"2020","journal-title":"Vet. Comp. Oncol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2460\/javma.20.12.0698","article-title":"Distribution of Histopathologic Types of Primary Pulmonary Neoplasia in Dogs and Outcome of Affected Dogs: 340 Cases (2010\u20132019)","volume":"260","author":"McPhetridge","year":"2022","journal-title":"J. Am. Vet. Med. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1111\/jvim.16739","article-title":"Prognosis of Primary Pulmonary Adenocarcinoma after Surgical Resection in Small-Breed Dogs: 52 Cases (2005\u20132021)","volume":"37","author":"Ichimata","year":"2023","journal-title":"J. Vet. Intern. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.2460\/javma.228.11.1718","article-title":"Comparison of Results of Computed Tomography and Radiography with Histopathologic Findings in Tracheobronchial Lymph Nodes in Dogs with Primary Lung Tumors: 14 Cases (1999\u20132002)","volume":"228","author":"Paoloni","year":"2006","journal-title":"J. Am. Vet. Med. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1177\/0300985819846870","article-title":"Feline Invasive Mammary Carcinomas: Prognostic Value of Histological Grading","volume":"56","author":"Dagher","year":"2019","journal-title":"Vet. Pathol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1177\/0300985814543198","article-title":"Prognostic Value of Histologic Grading for Feline Mammary Carcinoma: A Retrospective Survival Analysis","volume":"52","author":"Mills","year":"2015","journal-title":"Vet. Pathol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1111\/vco.12410","article-title":"Prognostic Significance of Histopathology in Canine Anal Sac Gland Adenocarcinomas: Preliminary Results in a Retrospective Study of 39 Cases","volume":"16","author":"Pradel","year":"2018","journal-title":"Vet. Comp. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1177\/0300985818800028","article-title":"Grading Cutaneous Mast Cell Tumors in Cats","volume":"56","author":"Sabattini","year":"2019","journal-title":"Vet. Pathol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jcpa.2015.04.005","article-title":"Interobserver Reproducibility of Histological Grading of Canine Simple Mammary Carcinomas","volume":"153","author":"Santos","year":"2015","journal-title":"J. Comp. Pathol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.tvjl.2014.03.019","article-title":"Nuclear Pleomorphism: Role in Grading and Prognosis of Canine Mammary Carcinomas","volume":"200","author":"Santos","year":"2014","journal-title":"Vet. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1177\/03009858231189205","article-title":"Automated Diagnosis of 7 Canine Skin Tumors Using Machine Learning on H&amp;E-Stained Whole Slide Images","volume":"60","author":"Wilm","year":"2023","journal-title":"Vet. Pathol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12322","DOI":"10.1109\/ACCESS.2021.3049582","article-title":"Tumour Nuclear Morphometrics Predict Survival in Lung Adenocarcinoma","volume":"9","author":"Alsubaie","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1016\/j.ajpath.2022.08.006","article-title":"Deep Learning\u2013Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma","volume":"192","author":"Chuang","year":"2022","journal-title":"Am. J. Pathol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1177\/0300985820985138","article-title":"Stereology in Grading and Prognosis of Canine Cutaneous Mast Cell Tumors","volume":"58","author":"Casanova","year":"2021","journal-title":"Vet. Pathol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1177\/03009858211013712","article-title":"International Guidelines for Veterinary Tumor Pathology: A Call to Action","volume":"58","author":"Meuten","year":"2021","journal-title":"Vet. Pathol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aubreville, M., Bertram, C., Klopfleisch, R., and Maier, A. (2018, January 13). SlideRunner: A Tool for Massive Cell Annotations in Whole Slide Images. Proceedings of the Bildverarbeitung f\u00fcr die Medizin 2018, Erlangen, Germany. Informatik Aktuell.","DOI":"10.1007\/978-3-662-56537-7_81"},{"key":"ref_30","unstructured":"Hyndman, P.S., Kennedy, K., and Donovan, T.A. Histological Risk Factors for Survival in Dogs with Primary Epithelial Pulmonary Tumors Following Lung Lobectomy."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"Scikit-Image: Image Processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_32","first-page":"3","article-title":"Unet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"Volume 11045","author":"Zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Doll\u00e1r, P. (2020, January 13\u201319). Designing Network Design Spaces. Proceedings of the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_34","unstructured":"Pavel, I. (2024, January 27). Segmentation Models Pytorch. Available online: https:\/\/github.com\/qubvel\/segmentation_models.pytorch."},{"key":"ref_35","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc.. Available online: http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf."},{"key":"ref_36","unstructured":"William, F. (2024, January 27). PyTorch Lightning. Available online: https:\/\/lightning.ai\/docs\/pytorch\/stable\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W., and Frangi, A. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_39","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA."},{"key":"ref_40","unstructured":"Haghofer, A., Parlak, E., Bartel, A., Donovan, T.A., Assenmacher, C.-A., Bolfa, P., Dark, M.J., Fuchs-Baumgartinger, A., Klang, A., and J\u00e4ger, K. (2023). Nuclear Pleomorphism in Canine Cutaneous Mast Cell Tumors-Comparison of Reproducibility and Prognostic Relevance between Estimates, Manual Morphometry and Algorithmic Morphometry. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1177\/03009858211067478","article-title":"Computer-Assisted Mitotic Count Using a Deep Learning\u2013Based Algorithm Improves Interobserver Reproducibility and Accuracy","volume":"59","author":"Bertram","year":"2022","journal-title":"Vet. Pathol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1177\/0300985820980049","article-title":"Mitotic Figures\u2014Normal, Atypical, and Imposters: A Guide to Identification","volume":"58","author":"Donovan","year":"2021","journal-title":"Vet. Pathol."},{"key":"ref_43","unstructured":"Owen, L.N. (1980). TNM Classification of Tumours in Domestic Animals, World Health Organization."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"23","DOI":"10.20982\/tqmp.08.1.p023","article-title":"Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial","volume":"8","author":"Hallgren","year":"2012","journal-title":"Tutor. Quant. Methods Psychol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1177\/0300985810379428","article-title":"Classification of Canine Malignant Lymphomas According to the World Health Organization Criteria","volume":"48","author":"Valli","year":"2011","journal-title":"Vet. Pathol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1177\/0300985817695781","article-title":"Practical Stereology Applications for the Pathologist","volume":"54","author":"Brown","year":"2017","journal-title":"Vet. Pathol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chuang, W.Y., Chang, S.H., Yu, W.H., Yang, C.K., Yeh, C.J., Ueng, S.H., Liu, Y.J., Chen, T.D., Chen, K.H., and Hsieh, Y.Y. (2020). Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning. Cancers, 12.","DOI":"10.3390\/cancers12020507"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1038\/s41379-021-00838-2","article-title":"Identification of Nodal Micrometastasis in Colorectal Cancer Using Deep Learning on Annotation-Free Whole-Slide Images","volume":"34","author":"Chuang","year":"2021","journal-title":"Mod. Pathol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1038\/s41467-021-21467-y","article-title":"An Annotation-Free Whole-Slide Training Approach to Pathological Classification of Lung Cancer Types Using Deep Learning","volume":"12","author":"Chen","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Steinbuss, G., Kriegsmann, M., Zgorzelski, C., Brobeil, A., Goeppert, B., Dietrich, S., Mechtersheimer, G., and Kriegsmann, K. (2021). Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images. Cancers, 13.","DOI":"10.3390\/cancers13102419"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19436","DOI":"10.1038\/s41598-023-46607-w","article-title":"Histological Classification of Canine and Feline Lymphoma Using a Modular Approach Based on Deep Learning and Advanced Image Processing","volume":"13","author":"Haghofer","year":"2023","journal-title":"Sci. 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      <first_name>Imaine</first_name>
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      <last_name>Assenmacher</last_name>
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      <first_name>Florian</first_name>
      <last_name>Bartenschlager</last_name>
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      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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    <abstract language="eng">Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation ( n = 35 WSIs), and test sets ( n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.</abstract>
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    <author>
      <first_name>Marco</first_name>
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      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
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      <first_name>Christof</first_name>
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      <first_name>Sophie</first_name>
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      <first_name>Anja</first_name>
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      <first_name>Taryn</first_name>
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      <first_name>Andrea</first_name>
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      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
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      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
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      <first_name>Laura</first_name>
      <last_name>Diehl</last_name>
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      <first_name>Chloe</first_name>
      <last_name>Puget</last_name>
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      <first_name>Andreas</first_name>
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      <first_name>Marc</first_name>
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      <first_name>Katharina</first_name>
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      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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      <value>skin</value>
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    </author>
    <author>
      <first_name>Jingxin</first_name>
      <last_name>Liu</last_name>
    </author>
    <author>
      <first_name>Salar</first_name>
      <last_name>Razavi</last_name>
    </author>
    <author>
      <first_name>April</first_name>
      <last_name>Khademi</last_name>
    </author>
    <author>
      <first_name>Sen</first_name>
      <last_name>Yang</last_name>
    </author>
    <author>
      <first_name>Xiyue</first_name>
      <last_name>Wang</last_name>
    </author>
    <author>
      <first_name>Ramona</first_name>
      <last_name>Erber</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
    </author>
    <author>
      <first_name>Karoline</first_name>
      <last_name>Lipnik</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
    </author>
    <author>
      <first_name>Gabriel</first_name>
      <last_name>Wasinger</last_name>
    </author>
    <author>
      <first_name>Mitko</first_name>
      <last_name>Veta</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Domain generalization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Histopathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Challenge</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mitosis</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
  </doc>
  <doc>
    <id>1916</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>582</pageFirst>
    <pageLast>590</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-03-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Iterative Cross-Scanner Registration for Whole Slide Images</title>
    <parentTitle language="eng">2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)</parentTitle>
    <identifier type="issn">2473-9944</identifier>
    <identifier type="isbn">978-1-6654-0191-3</identifier>
    <note>Eine frei zugängliche Version ist veröffentlicht unter: https://openaccess.thecvf.com/content/ICCV2021W/CDPath/html/Theelke_Iterative_Cross-Scanner_Registration_for_Whole_Slide_Images_ICCVW_2021_paper.html</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICCVW54120.2021.00071</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), online, 11.-17.10.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Luisa</first_name>
      <last_name>Theelke</last_name>
    </author>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer vision</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image resolution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Conferences</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Registers</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
  </doc>
  <doc>
    <id>1243</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>3</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>MIDL Foundation</publisherName>
    <publisherPlace>Nijmegen</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantifying the Scanner-Induced Domain Gap in Mitosis Detection</title>
    <parentTitle language="deu">MIDL: Medical Imaging with Deep Learning 2021</parentTitle>
    <identifier type="url">https://2021.midl.io/papers/i6</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">Medical Imaging with Deep Learning (MIDL), online, 07.-09.07.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Nikolas</first_name>
      <last_name>Stathonikos</last_name>
    </author>
    <author>
      <first_name>Natalie</first_name>
      <last_name>ter Hoeve</last_name>
    </author>
    <author>
      <first_name>Francesco</first_name>
      <last_name>Ciompi</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Mitko</first_name>
      <last_name>Veta</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
  </doc>
  <doc>
    <id>3519</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>75</pageFirst>
    <pageLast>85</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>60</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-04-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm</title>
    <abstract language="eng">Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of &lt; or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-35196</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1177/03009858221137582</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_DownloadUrl">https://journals.sagepub.com/doi/suppl/10.1177/03009858221137582/suppl_file/sj-pdf-1-vet-10.1177_03009858221137582.pdf</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
    </author>
    <author>
      <first_name>Jason</first_name>
      <last_name>Stayt</last_name>
    </author>
    <author>
      <first_name>Federico</first_name>
      <last_name>Bonsembiante</last_name>
    </author>
    <author>
      <first_name>Janet</first_name>
      <last_name>Beeler-Marfisi</last_name>
    </author>
    <author>
      <first_name>Ann K.</first_name>
      <last_name>Barton</last_name>
    </author>
    <author>
      <first_name>Ginevra</first_name>
      <last_name>Brocca</last_name>
    </author>
    <author>
      <first_name>Maria Elena</first_name>
      <last_name>Gelain</last_name>
    </author>
    <author>
      <first_name>Agnes C.</first_name>
      <last_name>Gläsel</last_name>
    </author>
    <author>
      <first_name>Kelly</first_name>
      <last_name>du Preez</last_name>
    </author>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Weiler</last_name>
    </author>
    <author>
      <first_name>Christiane</first_name>
      <last_name>Weissenbacher-Lang</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Jenny</first_name>
      <last_name>Hill</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated image analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bronchoalveolar lavage fluid</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>computational pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>equine</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>pulmonary hemorrhage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>respiratory disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>total hemosiderin score</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3519/03009858221137582.pdf</file>
  </doc>
  <doc>
    <id>1094</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue/>
    <volume>11</volume>
    <articleNumber>4343</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-01-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control</title>
    <abstract language="eng">In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10943</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-021-83827-4</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Jennifer</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Bergler</last_name>
    </author>
    <author>
      <first_name>Christine</first_name>
      <last_name>Kröger</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1094/s41598-021-83827-4.pdf</file>
  </doc>
  <doc>
    <id>1174</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>766</pageFirst>
    <pageLast>794</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>58</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">International Guidelines for Veterinary Tumor Pathology: A Call to Action</title>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1177/03009858211013712</enrichment>
    <enrichment key="THI_DownloadUrl">https://journals.sagepub.com/doi/suppl/10.1177/03009858211013712</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <author>
      <first_name>Donald J.</first_name>
      <last_name>Meuten</last_name>
    </author>
    <author>
      <first_name>Frances M.</first_name>
      <last_name>Moore</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Robert A.</first_name>
      <last_name>Foster</last_name>
    </author>
    <author>
      <first_name>Rebecca C.</first_name>
      <last_name>Smedley</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
    </author>
    <author>
      <first_name>Milan</first_name>
      <last_name>Milovancev</last_name>
    </author>
    <author>
      <first_name>Paul</first_name>
      <last_name>Stromberg</last_name>
    </author>
    <author>
      <first_name>Bruce H.</first_name>
      <last_name>Williams</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Giancarlo</first_name>
      <last_name>Avallone</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>John</first_name>
      <last_name>Cullen</last_name>
    </author>
    <author>
      <first_name>Michelle M.</first_name>
      <last_name>Dennis</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Goldschmidt</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Luong</last_name>
    </author>
    <author>
      <first_name>Andrew D.</first_name>
      <last_name>Miller</last_name>
    </author>
    <author>
      <first_name>Margaret A.</first_name>
      <last_name>Miller</last_name>
    </author>
    <author>
      <first_name>John S.</first_name>
      <last_name>Munday</last_name>
    </author>
    <author>
      <first_name>Paola</first_name>
      <last_name>Roccabianca</last_name>
    </author>
    <author>
      <first_name>Elisa N.</first_name>
      <last_name>Salas</last_name>
    </author>
    <author>
      <first_name>F. Yvonne</first_name>
      <last_name>Schulman</last_name>
    </author>
    <author>
      <first_name>Renee</first_name>
      <last_name>Laufer-Amorim</last_name>
    </author>
    <author>
      <first_name>Midori G.</first_name>
      <last_name>Asakawa</last_name>
    </author>
    <author>
      <first_name>Linden</first_name>
      <last_name>Craig</last_name>
    </author>
    <author>
      <first_name>Nick</first_name>
      <last_name>Dervisis</last_name>
    </author>
    <author>
      <first_name>D. Glen</first_name>
      <last_name>Esplin</last_name>
    </author>
    <author>
      <first_name>Jeanne W.</first_name>
      <last_name>George</last_name>
    </author>
    <author>
      <first_name>Marlene</first_name>
      <last_name>Hauck</last_name>
    </author>
    <author>
      <first_name>Yumiko</first_name>
      <last_name>Kagawa</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
    </author>
    <author>
      <first_name>Keith</first_name>
      <last_name>Linder</last_name>
    </author>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Meichner</last_name>
    </author>
    <author>
      <first_name>Laura</first_name>
      <last_name>Marconato</last_name>
    </author>
    <author>
      <first_name>Michelle L.</first_name>
      <last_name>Oblak</last_name>
    </author>
    <author>
      <first_name>Renato L.</first_name>
      <last_name>Santos</last_name>
    </author>
    <author>
      <first_name>R. Mark</first_name>
      <last_name>Simpson</last_name>
    </author>
    <author>
      <first_name>Harold</first_name>
      <last_name>Tvedten</last_name>
    </author>
    <author>
      <first_name>Derick</first_name>
      <last_name>Whitley</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>standardization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>oncology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>guidelines</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>protocols</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>validation</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
  </doc>
</export-example>
