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    <title language="eng">Automated diagnosis of 7 canine skin tumors using machine learning on H&amp;E-stained whole slide images</title>
    <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>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
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    <author>
      <first_name>Marco</first_name>
      <last_name>Fragoso-Garcia</last_name>
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      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
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      <first_name>Anja</first_name>
      <last_name>Schmidt</last_name>
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      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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      <first_name>Andrea</first_name>
      <last_name>Fuchs-Baumgartinger</last_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>
      <last_name>Maier</last_name>
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      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
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    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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      <value>computer-aided diagnosis</value>
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      <value>machine learning</value>
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      <type>uncontrolled</type>
      <value>skin</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>veterinary oncology</value>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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    <title language="eng">Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation</title>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Thomas Martin</first_name>
      <last_name>Deserno</last_name>
    </editor>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <editor>
      <first_name>Heinz</first_name>
      <last_name>Handels</last_name>
    </editor>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <editor>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </editor>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <editor>
      <first_name>Klaus H.</first_name>
      <last_name>Maier-Hein</last_name>
    </editor>
    <editor>
      <first_name>Christoph</first_name>
      <last_name>Palm</last_name>
    </editor>
    <editor>
      <first_name>Thomas</first_name>
      <last_name>Tolxdorff</last_name>
    </editor>
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  <doc>
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    <language>eng</language>
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    <issue>84</issue>
    <volume>2023</volume>
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    <publisherPlace>Amsterdam</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-12-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mitosis domain generalization in histopathology images — The MIDOG challenge</title>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="issn">1361-8415</identifier>
    <identifier type="isbn">1361-8423</identifier>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <author>
      <first_name>Nikolas</first_name>
      <last_name>Stathonikos</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>Natalie ter</first_name>
      <last_name>Hoeve</last_name>
    </author>
    <author>
      <first_name>Francesco</first_name>
      <last_name>Ciompi</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>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Jack</first_name>
      <last_name>Breen</last_name>
    </author>
    <author>
      <first_name>Nishant</first_name>
      <last_name>Ravikumar</last_name>
    </author>
    <author>
      <first_name>Youjin</first_name>
      <last_name>Chung</last_name>
    </author>
    <author>
      <first_name>Jinah</first_name>
      <last_name>Park</last_name>
    </author>
    <author>
      <first_name>Ramin</first_name>
      <last_name>Nateghi</last_name>
    </author>
    <author>
      <first_name>Fattaneh</first_name>
      <last_name>Pourakpour</last_name>
    </author>
    <author>
      <first_name>Rutger H. J.</first_name>
      <last_name>Fick</last_name>
    </author>
    <author>
      <first_name>Saima</first_name>
      <last_name>Ben Hadj</last_name>
    </author>
    <author>
      <first_name>Mostafa</first_name>
      <last_name>Jahanifar</last_name>
    </author>
    <author>
      <first_name>Adam</first_name>
      <last_name>Shepard</last_name>
    </author>
    <author>
      <first_name>Jakob</first_name>
      <last_name>Dexl</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Wittenberg</last_name>
    </author>
    <author>
      <first_name>Satoshi</first_name>
      <last_name>Kondo</last_name>
    </author>
    <author>
      <first_name>Maxime W.</first_name>
      <last_name>Lafarge</last_name>
    </author>
    <author>
      <first_name>Viktor H.</first_name>
      <last_name>Kolezer</last_name>
    </author>
    <author>
      <first_name>Jingtang</first_name>
      <last_name>Liang</last_name>
    </author>
    <author>
      <first_name>Yubo</first_name>
      <last_name>Wang</last_name>
    </author>
    <author>
      <first_name>Xi</first_name>
      <last_name>Long</last_name>
    </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/>
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    <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>
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    <enrichment key="THI_conferenceName">2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), online, 11.-17.10.2021</enrichment>
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    <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>
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    <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>
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    <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>
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      <value>computational pathology</value>
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      <value>digital pathology</value>
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      <value>equine</value>
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      <language>eng</language>
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      <value>pulmonary hemorrhage</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>respiratory disease</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>total hemosiderin score</value>
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    <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>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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      <first_name>Jennifer</first_name>
      <last_name>Maier</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Bergler</last_name>
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      <first_name>Christine</first_name>
      <last_name>Kröger</last_name>
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      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
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      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
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    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
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    <title language="eng">Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?</title>
    <abstract language="eng">Canine cutaneous mast cell tumors (ccMCTs) are frequent neoplasms with variable biological behaviors. Internal tandem duplication mutations in c-KIT exon 11 (c-KIT-11-ITD) are associated with poor prognosis but predict therapeutic response to tyrosine kinase inhibitors. In a previous work, deep learning algorithms managed to predict the presence of c-KIT-11-ITD on digitalized hematoxylin and eosin-stained histological slides (whole-slide images, WSIs) in up to 87% of cases, suggesting the existence of morphological features characterizing ccMCTs carrying c-KIT-11-ITD. This 3-stage blinded study aimed to identify morphological features indicative of c-KIT-11-ITD and to evaluate the ability of human observers to learn this task. 17 untrained pathologists first classified 8 WSIs and 200 image patches (highly relevant for algorithmic classification) of ccMCTs as either positive or negative for c-KIT-11-ITD. Second, they self-trained to recognize c-KIT-11-ITD by looking at the same WSIs and patches correctly sorted. Third, pathologists classified 15 new WSIs and 200 new patches according to c-KIT-11-ITD status. In addition, participants reported microscopic features they considered relevant for their decision. Without training, participants correctly classified the c-KIT-11-ITD status of 63%–88% of WSIs and 43%–55% of patches. With self-training, 25%–38% of WSIs and 55%–56% of patches were correctly classified. High cellular pleomorphism, anisokaryosis, and sparse cytoplasmic granulation were commonly suggested as features associated with c-KIT-11-ITD-positive ccMCTs, none of which showed reliable predictivity in a follow-up study. The results indicate that transfer of algorithmic skills to the human observer is difficult. A c-KIT-11-ITD-specific morphological feature remains to be extracted from the artificial intelligence model.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
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    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
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      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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      <first_name>Thomas</first_name>
      <last_name>Conrad</last_name>
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      <first_name>Malte</first_name>
      <last_name>Baeblich</last_name>
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      <first_name>Anne</first_name>
      <last_name>Voss</last_name>
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      <first_name>Katharina</first_name>
      <last_name>Landmann</last_name>
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    <author>
      <first_name>Alexander F. H.</first_name>
      <last_name>Haake</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Spree</last_name>
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    <author>
      <first_name>Svenja</first_name>
      <last_name>Hartung</last_name>
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      <first_name>Leonore</first_name>
      <last_name>Aeschlimann</last_name>
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      <first_name>Sara</first_name>
      <last_name>Soto</last_name>
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      <first_name>Simone</first_name>
      <last_name>de Brot</last_name>
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      <first_name>Martina</first_name>
      <last_name>Dettwiler</last_name>
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    <author>
      <first_name>Heike</first_name>
      <last_name>Aupperle-Lellbach</last_name>
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    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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    <author>
      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
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    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6384/puget_artificial-intelligence-predicts.pdf</file>
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  <doc>
    <id>5332</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst>152</pageFirst>
    <pageLast>160</pageLast>
    <pageNumber/>
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    <issue>2</issue>
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    <completedDate>2024-11-15</completedDate>
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    <title language="eng">Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides</title>
    <abstract language="eng">Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene ( c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c- KIT mutations in HE slides.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <identifier type="issn">0300-9858</identifier>
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In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene ( c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. 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    <abstract language="eng">Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.</abstract>
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    <title language="eng">A comprehensive multi-domain dataset for mitotic figure detection</title>
    <abstract language="eng">The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.</abstract>
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