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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>
    <enrichment key="THI_openaccess">ja</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>4154</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</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">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>
    <identifier type="urn">urn:nbn:de:bvb:573-41549</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2311.08949</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5817</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Moritz</first_name>
      <last_name>Hecker</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</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>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/4154/2311.08949.pdf</file>
  </doc>
  <doc>
    <id>4152</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</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">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>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2311.07216</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <licence>Creative Commons BY-SA 4.0</licence>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Zhaoya</first_name>
      <last_name>Pan</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Ann-Kathrin</first_name>
      <last_name>Frenken</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</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/4152/2311.07216.pdf</file>
  </doc>
  <doc>
    <id>3916</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</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">Würstchen: Efficient Pretraining of Text-to-Image Models</title>
    <abstract language="eng">We introduce Wuerstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardware. Building on recent advancements in machine learning, our approach, which utilizes latent diffusion strategies at strong latent image compression rates, significantly reduces the computational burden, typically associated with state-of-the-art models, while preserving, if not enhancing, the quality of generated images. Wuerstchen achieves notable speed improvements at inference time, thereby rendering real-time applications more viable. One of the key advantages of our method lies in its modest training requirements of only 9,200 GPU hours, slashing the usual costs significantly without compromising the end performance. In a comparison against the state-of-the-art, we found the approach to yield strong competitiveness. This paper opens the door to a new line of research that prioritizes both performance and computational accessibility, hence democratizing the use of sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling stride forward in the realm of text-to-image synthesis, offering an innovative path to explore in future research.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-39160</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2306.00637</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Pablo</first_name>
      <last_name>Pernias</last_name>
    </author>
    <author>
      <first_name>Dominic</first_name>
      <last_name>Rampas</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/3916/2306.00637.pdf</file>
  </doc>
  <doc>
    <id>4141</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume>13</volume>
    <articleNumber>19436</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing</title>
    <abstract language="eng">AbstractHistopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
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    <abstract language="eng">Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.</abstract>
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