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  <doc>
    <id>8968</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e64</pageFirst>
    <pageLast>e65</pageLast>
    <pageNumber/>
    <edition/>
    <issue>03</issue>
    <volume>64</volume>
    <type>article</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
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    <title language="deu">KI-gestützte Detektion und Segmentierung von Magenkarzinomen in westlichen endoskopischen Bilddaten anhand eines fine-tuned Deep-Learning Ansatzes</title>
    <abstract language="deu">Diese vorläufige monozentrische Studie zeigt, dass ein aus einem Barrett-Ösophagus-KI-System feinjustiertes Deep-Learning-Modell Magenkarzinome in westlichen multimodalen endoskopischen Bilddaten zuverlässig detektieren und präzise segmentieren kann. Die hohe Segmentierungsgenauigkeit und Detektionssensitivität über verschiedene Bildmodalitäten hinweg unterstreichen die Machbarkeit eines pathologiegestützten KI-Ansatzes auch in einer westlichen Niedriginzidenzpopulation. Aufgrund der ausschließlichen Verwendung von Bildern mit sichtbaren Tumoren lassen sich keine Aussagen zur Spezifität treffen; eine Übertragbarkeit auf Screening- oder Mischkollektive ist daher limitiert. Weitere Studien mit a) größerem Datensatz inklusive Videodaten, b) externer Validierung an einer multizentrischen westlichen Kohorte, sowie c) Anwendung und Prüfung an nicht-neoplastischen Vergleichsbildern oder anderen Pathologien sind erforderlich.&#13;
&#13;
Nach unserem Kenntnisstand zählt dieses System zu den ersten in einer westlichen Population entwickelten endoskopischen KI-Ansätzen zur Magenkarzinomdetektion, und zu wenigen, die vollständige ESD-präparatbasierte Referenzdaten für Training und Validierung nutzen.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0046-1817751</identifier>
    <enrichment key="opus.import.date">2026-03-29T15:41:42+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>M. Wallner</author>
    <author>Max Gutbrod</author>
    <author>David Rauber</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>David Roser</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
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  </doc>
  <doc>
    <id>8979</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>95</pageFirst>
    <pageLast>114</pageLast>
    <pageNumber/>
    <edition/>
    <issue>MELBA–BVM 2025 Special Issue</issue>
    <volume/>
    <type>article</type>
    <publisherName>Melba</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videos</title>
    <abstract language="eng">We present a neural parametric 3D breast shape model and, based on this model, introduce a low-cost and accessible 3D surface reconstruction pipeline capable of recovering accurate breast geometry from a monocular RGB video. In contrast to widely used, commercially available yet expensive 3D breast scanning solutions and existing low-cost alternatives, our method requires neither specialized hardware nor proprietary software and can be used with any device that is able to record RGB videos. The key building blocks of our pipeline are a state-of-the-art, off-the-shelf Structure-from-Motion pipeline, paired with a parametric breast model for robust surface reconstruction. Our model, similarly to the recently proposed implicit Regensburg Breast Shape Model (iRBSM), leverages implicit neural representations to model breast shapes. However, unlike the iRBSM, which employs a single global neural Signed Distance Function (SDF), our approach—inspired by recent state-of-the-art face models—decomposes the implicit breast domain into multiple smaller regions, each represented by a local neural SDF anchored at anatomical landmark positions. When incorporated into our surface reconstruction pipeline, the proposed model, dubbed liRBSM (short for localized iRBSM), significantly outperforms the iRBSM in terms of reconstruction quality, yielding more detailed surface reconstruction than its global counterpart. Overall, we find that the introduced pipeline is able to recover high-quality and metrically correct 3D breast geometry within an error margin of less than 2 mm. Our method is fast (requires less than six minutes), fully transparent and open-source, and together with the model publicly available at https://rbsm.re-mic.de/local-implicit.</abstract>
    <parentTitle language="eng">Machine Learning for Biomedical Imaging (MELBA)</parentTitle>
    <identifier type="doi">10.59275/j.melba.2026-8b23</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-89791</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Maximilian Weiherer</author>
    <author>Antonia von Riedheim</author>
    <author>Vanessa Brébant</author>
    <author>Bernhard Egger</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D Reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Shape Modeling</value>
    </subject>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Diamond Open Access - OA-Veröffentlichung ohne Publikationskosten (Sponsoring)</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Gesundheit und Soziales</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/8979/2026_005_.pdf</file>
  </doc>
  <doc>
    <id>8976</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>362</pageFirst>
    <pageLast>367</pageLast>
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    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving Generalization in Mitotic Cell Detection via Domain Transformations</title>
    <abstract language="eng">We address domain generalization (DG) in mitotic-cell (MC) detection by combining a β-variational autoencoder (VAE) for domain transformations with feature-space alignment together with an object detector. The β-VAE synthesizes domain-transformed images, and the detector is trained to map originals and their transformed counterparts to equal representations. On the MIDOG++ dataset, this approach improves out-of-domain detection F1 scores by 7 and 3 percentage points compared to the color-variation augmentation and stain-normalization baselines. Results further suggest that morphology shifts hinder generalization more than stain shifts.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Lübeck March 15-17, 2026</parentTitle>
    <identifier type="doi">10.1007/978-3-658-51100-5_71</identifier>
    <enrichment key="OtherSeries">Informatik aktuell</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="OtherSeries">BVM Workshop</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Max Gutbrod</author>
    <author>David Rauber</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildverarbeitung</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
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  </doc>
  <doc>
    <id>8977</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>131</pageFirst>
    <pageLast>131</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferencepresentation</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Abstract: DIY Challenge Blueprint</title>
    <abstract language="eng">The high cost of challenge platforms prevents many people from organizing their own competitions. The do-it-yourself (DIY) challenge blueprint [1] allows you to host your own biomedical AI benchmark challenge. Our DIY approach circumvents the current constraints of commercial challenge platforms. A sovereign, extensible and cost-efficient deployment is provided via containerised, identity-managed and reproducible pipelines. Focus lies on GDPR-compliant hosting via infrastructure-as-code, automated evaluation, modular orchestration, and role-based identity and access management. The framework integrates Docker-based execution and standardised interfaces for task definitions, dataset curation and evaluation. All in all it is designed to be flexible and modular, as demonstrated in the MICCAI 2024 PhaKIR challenge [2, 3]. In this case study, different medical tasks on a multicentre laparoscopic dataset with framewise labels for phases and spatial annotations for instruments across fulllength videos were supported. This case study empirically validates the DIY challenge blueprint as a reproducible and customizable challenge-hosting infrastructure. The full code can be found at https://github.com/remic-othr/PhaKIR_DIY.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Lübeck March 15-17, 2026</parentTitle>
    <subTitle language="deu">from organization to technical implementation in Biomedical Image Analysis</subTitle>
    <identifier type="doi">10.1007/978-3-658-51100-5_27</identifier>
    <enrichment key="OtherSeries">Informatik aktuell</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="OtherSeries">BVM Workshop</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Leonard Klausmann</author>
    <author>Tobias Rueckert</author>
    <author>David Rauber</author>
    <author>Raphaela Maerkl</author>
    <author>Suemeyye R. Yildiran</author>
    <author>Max Gutbrod</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildverarbeitung</value>
    </subject>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Gesundheit und Soziales</collection>
  </doc>
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