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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 consensus 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 F1 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. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, but with only minor changes in the order of participants in the ranking.</abstract>
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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">"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>
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    <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>
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    <editor>
      <first_name>Phoebe</first_name>
      <last_name>Toups-Dugas</last_name>
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    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
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    <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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      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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      <first_name>Georgios</first_name>
      <last_name>Paraschou</last_name>
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    <author>
      <first_name>Mun Keong</first_name>
      <last_name>Kok</last_name>
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    <author>
      <first_name>Alexander F. H.</first_name>
      <last_name>Haake</last_name>
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    <author>
      <first_name>Ronald R.</first_name>
      <last_name>de Krijger</last_name>
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    <author>
      <first_name>Andreas F.-P.</first_name>
      <last_name>Sonnen</last_name>
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    <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>
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    <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>
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  <doc>
    <id>5969</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue/>
    <volume/>
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    <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>
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    <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>
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    <author>
      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
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    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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    <author>
      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
    </author>
    <author>
      <first_name>Maximilian C.</first_name>
      <last_name>Köller</last_name>
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    <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>
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    <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="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>5968</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
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    <contributingCorporation/>
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    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
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    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</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>5652</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue>81</issue>
    <volume>2025</volume>
    <articleNumber>102791</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-02-10</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">An interdisciplinary perspective on AI-supported decision making in medicine</title>
    <abstract language="eng">Artificial intelligence (AI)-supported medical diagnosis offers the potential to utilize the collaborative intelligence of context-sensitive humans and narrowly focused machines for patients’ benefit. The employment of machine-learning-based decision-support systems (MLDSS) in medicine, however, raises important multidisciplinary challenges that cannot be addressed in isolation. We discuss three disciplinary perspectives on the topic and their interplay. Ethical issues arise at the level of changing responsibility structures in healthcare. Behavioral issues relate to the actual impact that the system has on physicians. Technical issues arise with respect to the training of a machine learning (ML) model that gives accurate advice. We argue that the interaction between physicians and MLDSS including the concrete design of the interface in which this interaction occurs can only be considered at the intersection of all three disciplines.</abstract>
    <parentTitle language="eng">Technology in Society</parentTitle>
    <identifier type="issn">1879-3274</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-56526</identifier>
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    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Alexis</first_name>
      <last_name>Fritz</last_name>
    </author>
    <author>
      <first_name>Angelika</first_name>
      <last_name>Kießig</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Krügel</last_name>
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    <author>
      <first_name>Matthias</first_name>
      <last_name>Uhl</last_name>
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    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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  </doc>
  <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>
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    <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>
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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>
    </author>
    <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>5967</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
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    <volume/>
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    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification</title>
    <abstract language="eng">Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of labeled data, and increasing domain robustness. In this work, we investigate to which degree this also holds for mitotic figure classification. Utilizing two popular public mitotic figure datasets, we compared linear probing of five publicly available FMs against models trained on ImageNet and a simple ResNet50 end-to-end-trained baseline. We found that the end-to-end-trained baseline outperformed all FM-based classifiers, regardless of the amount of data provided. Additionally, we did not observe the FM-based classifiers to be more robust against domain shifts, rendering both of the above assumptions incorrect.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59672</identifier>
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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>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</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>
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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;
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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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
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    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
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    <author>
      <first_name>Barbara</first_name>
      <last_name>Richter</last_name>
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      <first_name>Chloé</first_name>
      <last_name>Puget</last_name>
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    <author>
      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
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    <author>
      <first_name>Elena A.</first_name>
      <last_name>Demeter</last_name>
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    <author>
      <first_name>Flaviu A.</first_name>
      <last_name>Tabaran</last_name>
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    <author>
      <first_name>Gabriel</first_name>
      <last_name>Wasinger</last_name>
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      <first_name>Karoline</first_name>
      <last_name>Lipnik</last_name>
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      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
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    <author>
      <first_name>Matthew J.</first_name>
      <last_name>Valentine</last_name>
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    <author>
      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
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      <last_name>Abele</last_name>
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      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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      <last_name>Erber</last_name>
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      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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      <last_name>Merz</last_name>
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      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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      <first_name>Samir</first_name>
      <last_name>Jabari</last_name>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <author>
      <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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    <id>4154</id>
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    <publishedYear>2023</publishedYear>
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    <pageNumber>6</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <title language="eng">Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard</title>
    <abstract language="eng">The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that using a deep learning pipeline solely trained with an annotation-free, immunohistochemistry-based approach, provides accurate estimations of epithelial segmentation in canine breast carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility.</abstract>
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    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Moritz</first_name>
      <last_name>Hecker</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <author>
      <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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    <id>3064</id>
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    <publishedYear>2022</publishedYear>
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    <pageNumber>7</pageNumber>
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    <title language="eng">Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays</title>
    <abstract language="eng">Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients.&#13;
The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage.</abstract>
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    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Lars-Henning</first_name>
      <last_name>Schmidt</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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    <author>
      <first_name>Tanja</first_name>
      <last_name>Niedermair</last_name>
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    <author>
      <first_name>Christoph</first_name>
      <last_name>Brochhausen-Delius</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Schulz</last_name>
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    <author>
      <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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    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3064/2212.07724.pdf</file>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <title language="eng">Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors</title>
    <abstract language="eng">The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6% on the rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-41522</identifier>
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    <author>
      <first_name>Marc</first_name>
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      <first_name>Zhaoya</first_name>
      <last_name>Pan</last_name>
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      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
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    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
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    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
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    <author>
      <first_name>Ann-Kathrin</first_name>
      <last_name>Frenken</last_name>
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    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4152/2311.07216.pdf</file>
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    <title language="eng">Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images</title>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023</parentTitle>
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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">Re-identification from histopathology images</title>
    <abstract language="eng">In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. In addition, we compared a comprehensive set of state-of-the-art whole slide image classifiers and feature extractors for the given task. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm’s performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with 𝐹1 scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient’s privacy prior to publication.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="issn">1361-8423</identifier>
    <identifier type="issn">1361-8415</identifier>
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    <abstract language="eng">In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes.</abstract>
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    <title language="eng">Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology</title>
    <abstract language="eng">Artificial intelligence (AI)-driven clinical decision support systems (CDSS) hold promise to improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may give rise to cognitive biases, such as automation and anchoring bias, wherein users may be inclined to blindly adopt system recommendations or be disproportionately influenced by the presence of AI predictions, even when they are inaccurate. These biases may be exacerbated under time pressure, pervasive in routine pathology diagnostics, or shaped by individual user characteristics. To investigate these effects, we conducted a web-based experiment in which trained pathology experts (n = 28) estimated tumor cell percentages twice: once independently and once with the aid of an AI. A subset of the estimates in each condition was performed under time constraints. Our findings indicate that AI integration generally enhances diagnostic performance. However, it also introduced a 7% automation bias rate, quantified as the number of accepted negative consultations, where a previously correct independent assessment gets overturned by inaccurate AI guidance. While time pressure did not increase the frequency of automation bias occurrence, it appeared to intensify its severity, as evidenced by a performance decline linked to increased automation reliance under cognitive load. A linear mixed-effects model (LMM) analysis, simulating weighted averaging, revealed a statistically significant positive coefficient for AI advice, indicating a moderate degree of anchoring on system output. This effect was further intensified under time pressure, suggesting that anchoring bias may become more pronounced when cognitive resources are limited. A secondary LMM evaluation assessing automation reliance, used as a proxy for both automation and anchoring bias, demonstrated that professional experience and self-efficacy were associated with reduced dependence on system support, whereas higher confidence during AI-assisted decision-making was linked to increased automation reliance. Together, these findings underscore the dual nature of AI integration in clinical workflows, offering performance benefits while also introducing risks of cognitive bias–driven diagnostic errors. As an initial investigation focused on a single medical specialty and diagnostic task, this study aims to lay the groundwork for future research to explore these phenomena across diverse clinical contexts, ultimately supporting the establishment of appropriate reliance on automated systems and the safe, effective integration of human–AI collaboration in medical decision-making.</abstract>
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      <first_name>Emely</first_name>
      <last_name>Rosbach</last_name>
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      <first_name>Jonas</first_name>
      <last_name>Ammeling</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>
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      <last_name>Conrad</last_name>
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      <first_name>Andreas</first_name>
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      <first_name>Marc</first_name>
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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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    <identifier type="issn">2052-4463</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-40068</identifier>
    <note>Author Correction verfügbar unter https://doi.org/10.1038/s41597-024-03548-x</note>
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