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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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    <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>
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      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
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      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
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    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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    <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>
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    <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>5969</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">"When TwoWrongs Don’t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology</title>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5879</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2411.01007</enrichment>
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    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Krügel</last_name>
    </author>
    <author>
      <first_name>Angelika</first_name>
      <last_name>Kießig</last_name>
    </author>
    <author>
      <first_name>Alexis</first_name>
      <last_name>Fritz</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <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>
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    <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="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>
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  <doc>
    <id>5015</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On the Value of PHH3 for Mitotic Figure Detection on H&amp;E-stained Images</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. Deep learning algorithms can standardize this task, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithm's performance. Unlike H&amp;E, 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 stain alone, the use of this ground truth could potentially introduce 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. We found that the annotators' object-level agreement increased when using PHH3-assisted labeling. Subsequently, MF detectors were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. Additionally, a novel dual-stain MF detector was developed to investigate the interpretation-shift of PHH3-assisted labels used in H&amp;E, which clearly outperformed single-stain detectors. However, the PHH3-assisted labels did not have a positive effect on solely H&amp;E-based models. The high performance of our dual-input detector reveals an information mismatch between the H&amp;E and PHH3-stained images as the cause of this effect.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-50155</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2406.19899</enrichment>
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    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Barbara</first_name>
      <last_name>Richter</last_name>
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    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
    </author>
    <author>
      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
    </author>
    <author>
      <first_name>Elena A.</first_name>
      <last_name>Demeter</last_name>
    </author>
    <author>
      <first_name>Flaviu A.</first_name>
      <last_name>Tabaran</last_name>
    </author>
    <author>
      <first_name>Gabriel</first_name>
      <last_name>Wasinger</last_name>
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    <author>
      <first_name>Karoline</first_name>
      <last_name>Lipnik</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
    </author>
    <author>
      <first_name>Matthew J.</first_name>
      <last_name>Valentine</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
    </author>
    <author>
      <first_name>Niklas</first_name>
      <last_name>Abele</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>Ramona</first_name>
      <last_name>Erber</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Samir</first_name>
      <last_name>Jabari</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</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>
    <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/5015/2406.19899v1-1.pdf</file>
  </doc>
  <doc>
    <id>4602</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
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    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides</title>
    <abstract language="eng">Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-46020</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2401.06169</enrichment>
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    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Julian</first_name>
      <last_name>Ostermaier</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Konrad</last_name>
    </author>
    <author>
      <first_name>Eda</first_name>
      <last_name>Parlak</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</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>
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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.&#13;
&#13;
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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. 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\u2019 performance. We found that the annotators\u2019 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;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;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;amp;E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.&lt;\/jats:p&gt;","DOI":"10.1038\/s41598-024-77244-6","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T05:03:23Z","timestamp":1730437403000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&amp;amp;E slide analysis"],"prefix":"10.1038","volume":"14","author":[{"given":"Jonathan","family":"Ganz","sequence":"first","affiliation":[]},{"given":"Christian","family":"Marzahl","sequence":"additional","affiliation":[]},{"given":"Jonas","family":"Ammeling","sequence":"additional","affiliation":[]},{"given":"Emely","family":"Rosbach","sequence":"additional","affiliation":[]},{"given":"Barbara","family":"Richter","sequence":"additional","affiliation":[]},{"given":"Chlo\u00e9","family":"Puget","sequence":"additional","affiliation":[]},{"given":"Daniela","family":"Denk","sequence":"additional","affiliation":[]},{"given":"Elena A.","family":"Demeter","sequence":"additional","affiliation":[]},{"given":"Flaviu A.","family":"T\u0103b\u0103ran","sequence":"additional","affiliation":[]},{"given":"Gabriel","family":"Wasinger","sequence":"additional","affiliation":[]},{"given":"Karoline","family":"Lipnik","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Tecilla","sequence":"additional","affiliation":[]},{"given":"Matthew J.","family":"Valentine","sequence":"additional","affiliation":[]},{"given":"Michael J.","family":"Dark","sequence":"additional","affiliation":[]},{"given":"Niklas","family":"Abele","sequence":"additional","affiliation":[]},{"given":"Pompei","family":"Bolfa","sequence":"additional","affiliation":[]},{"given":"Ramona","family":"Erber","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Klopfleisch","sequence":"additional","affiliation":[]},{"given":"Sophie","family":"Merz","sequence":"additional","affiliation":[]},{"given":"Taryn A.","family":"Donovan","sequence":"additional","affiliation":[]},{"given":"Samir","family":"Jabari","sequence":"additional","affiliation":[]},{"given":"Christof A.","family":"Bertram","sequence":"additional","affiliation":[]},{"given":"Katharina","family":"Breininger","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Aubreville","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"77244_CR1","doi-asserted-by":"publisher","first-page":"5993","DOI":"10.1200\/JCO.2005.05.511","volume":"23","author":"JP Baak","year":"2005","unstructured":"Baak, J. 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    <title language="eng">Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry</title>
    <abstract language="eng">The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor’s immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72–0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met.</abstract>
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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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      <first_name>Chloé</first_name>
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      <first_name>Katharina</first_name>
      <last_name>Landmann</last_name>
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      <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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      <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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      <first_name>Heike</first_name>
      <last_name>Aupperle-Lellbach</last_name>
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      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
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      <first_name>Matti</first_name>
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      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
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      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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      <first_name>Robert</first_name>
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    <file>https://opus4.kobv.de/opus4-haw/files/6384/puget_artificial-intelligence-predicts.pdf</file>
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    <publishedYear>2024</publishedYear>
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    <pageFirst>152</pageFirst>
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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>
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      <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>Julian</first_name>
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      <last_name>Conrad</last_name>
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      <first_name>Eda</first_name>
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      <first_name>Christof</first_name>
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      <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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      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</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/5332/Puget_artificial-intelligence.pdf</file>
  </doc>
  <doc>
    <id>2874</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue/>
    <volume>9</volume>
    <articleNumber>588</articleNumber>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-10-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset</title>
    <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>
    <parentTitle language="eng">Scientific Data</parentTitle>
    <identifier type="issn">2052-4463</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-28741</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41597-022-01692-w</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://github.com/DeepPathology/CanineCutaneousTumors</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Fragoso-Garcia</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Jingna</first_name>
      <last_name>Qiu</last_name>
    </author>
    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
    </author>
    <author>
      <first_name>Laura</first_name>
      <last_name>Diehl</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>Katharina</first_name>
      <last_name>Breininger</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="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2874/s41597-022-01692-w.pdf</file>
  </doc>
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