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  <doc>
    <id>4151</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>20</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <identifier type="urn">urn:nbn:de:bvb:573-41514</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2309.15589</enrichment>
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    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5847</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Nikolas</first_name>
      <last_name>Stathonikos</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <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>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Mitko</first_name>
      <last_name>Veta</last_name>
    </author>
    <author>
      <first_name>Samir</first_name>
      <last_name>Jabari</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Eckstein</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Annuscheit</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Krumnow</last_name>
    </author>
    <author>
      <first_name>Engin</first_name>
      <last_name>Bozaba</last_name>
    </author>
    <author>
      <first_name>Sercan</first_name>
      <last_name>Cayir</last_name>
    </author>
    <author>
      <first_name>Hongyan</first_name>
      <last_name>Gu</last_name>
    </author>
    <author>
      <first_name>Xiang</first_name>
      <last_name>Chen</last_name>
    </author>
    <author>
      <first_name>Mostafa</first_name>
      <last_name>Jahanifar</last_name>
    </author>
    <author>
      <first_name>Adam</first_name>
      <last_name>Shephard</last_name>
    </author>
    <author>
      <first_name>Satoshi</first_name>
      <last_name>Kondo</last_name>
    </author>
    <author>
      <first_name>Satoshi</first_name>
      <last_name>Kasai</last_name>
    </author>
    <author>
      <first_name>Sujatha</first_name>
      <last_name>Kotte</last_name>
    </author>
    <author>
      <first_name>Vangala</first_name>
      <last_name>Saipradeep</last_name>
    </author>
    <author>
      <first_name>Maxime W.</first_name>
      <last_name>Lafarge</last_name>
    </author>
    <author>
      <first_name>Viktor H.</first_name>
      <last_name>Koelzer</last_name>
    </author>
    <author>
      <first_name>Ziyue</first_name>
      <last_name>Wang</last_name>
    </author>
    <author>
      <first_name>Yongbing</first_name>
      <last_name>Zhang</last_name>
    </author>
    <author>
      <first_name>Sen</first_name>
      <last_name>Yang</last_name>
    </author>
    <author>
      <first_name>Xiyue</first_name>
      <last_name>Wang</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</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/4151/2309.15589.pdf</file>
  </doc>
  <doc>
    <id>4140</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>24</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors</title>
    <abstract language="eng">Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area ≥9.0μm2) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area ≥10.9μm2) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair (κ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-41401</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2309.15031</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <licence>Creative Commons BY-NC-SA 4.0</licence>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Haghofer</last_name>
    </author>
    <author>
      <first_name>Eda</first_name>
      <last_name>Parlak</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
    </author>
    <author>
      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
    </author>
    <author>
      <first_name>Charles-Antoine</first_name>
      <last_name>Assenmacher</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Fuchs-Baumgartinger</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
    </author>
    <author>
      <first_name>Kathrin</first_name>
      <last_name>Jäger</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>Barbara</first_name>
      <last_name>Richter</last_name>
    </author>
    <author>
      <first_name>F. Yvonne</first_name>
      <last_name>Schulman</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Josef</first_name>
      <last_name>Scharinger</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Stephan M.</first_name>
      <last_name>Winkler</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</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/4140/2309.15031.pdf</file>
  </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>
    <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>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <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>
    </author>
    <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>4602</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <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>
    </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/4602/2401.06169.pdf</file>
  </doc>
  <doc>
    <id>5763</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-03-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <parentTitle language="eng">Research Square</parentTitle>
    <identifier type="issn">2693-5015</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-57630</identifier>
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