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2026

Exploring General-purpose Autonomous Multimodal Agents for Pathology Report Generation

Aubreville, M., Donovan, T. A., & Bertram, C. A. (2026). Exploring General-purpose Autonomous Multimodal Agents for Pathology Report Generation. In H. Handels, K. Breininger, T. Deserno, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2026 (S. 393–399). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-51100-5_76
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Aubreville, Marc

Forschungsprojekt

(mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Data Science und Künstliche Intelligenz

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Fachbereich 3: Information und Kommunikation
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2026

Conference Paper

Marc Aubreville

Taryn Donovan

Christof Bertram

Heinz Handels

Katharina Breininger

Thomas Deserno

Andreas Maier

Klaus Maier-Hein

Christoph Palm

Thomas Tolxdorff
Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN

Banerjee, S., Gosch, T., Hester, S., Weiss, V., Conrad, T., Donovan, T. A., … Aubreville, M. (2026). Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN. In H. Handels, K. Breininger, T. Deserno, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2026 (S. 203–209). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-51100-5_42
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Banerjee, Sweta

Porsche, Nils

Aubreville, Marc

Forschungsprojekt

(mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Data Science und Künstliche Intelligenz

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Fachbereich 3: Information und Kommunikation
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2026

Conference Paper

Sweta Banerjee

Timo Gosch

Sara Hester

Viktoria Weiss

Thomas Conrad

Taryn Donovan

Nils Porsche

Jonas Ammeling

Christoph Stroblberger

Robert Klopfleisch

Christopher Kaltenecker

Christof Bertram

Katharina Breininger

Marc Aubreville

Heinz Handels

Thomas Deserno

Andreas Maier

Klaus Maier-Hein

Christoph Palm

Thomas Tolxdorff
Filtering Scheme for Confocal Laser Endomicroscopy (CLE)-video Sequences for Self-supervised Learning

Porsche, N., Müller-Diesing, F., Banerjee, S., Goncalves, M., & Aubreville, M. (2026). Filtering Scheme for Confocal Laser Endomicroscopy (CLE)-video Sequences for Self-supervised Learning. In H. Handels, K. Breininger, T. Deserno, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2026 (S. 375–380). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-51100-5_73
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Porsche, Nils

Banerjee, Sweta

Aubreville, Marc

Forschungsprojekt

Optical Biopsy of Sinonasal Tumors using Confocal Laser Endomicroscopy: A Clinical and Deep Learning-based Assessment and Visualization

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2026

Conference Paper

Nils Porsche

Flurin Müller-Diesing

Sweta Banerjee

Miguel Goncalves

Marc Aubreville

Heinz Handels

Katharina Breininger

Thomas Deserno

Andreas Maier

Klaus Maier-Hein

Christoph Palm

Thomas Tolxdorff

0

"Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN"

Banerjee, S. ". "Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN". In H. " Handels, K. Breininger, T. Deserno, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Hrsg.), "Bildverarbeitung für die Medizin 2026" (S. 203–209). "Wiesbaden": "Springer Fachmedien Wiesbaden".

Abstract

The annotation of large-scale histopathology image datasets remains a major bottleneck in developing robust deep learning models for clinically relevant tasks, such as mitotic figure classification. Folder-based annotation workflows are usually slow, fatiguing, and difficult to scale. To address these challenges, we introduce swipeable annotations (SWAN), an open-source, MIT-licensed web application that enables intuitive image patch classification using a swiping gesture. SWAN supports both desktop and mobile platforms, offers real-time metadata capture, and allows flexible mapping of swipe gestures to class labels. In a pilot study with four pathologists annotating 600 mitotic figure image patches,we compared SWANagainst a traditional folder-sorting workflow. SWAN enabled rapid annotations with pairwise percent agreement ranging from 86.52 \% to 93.68 \% (Cohen s $\kappa$ = 0.61—0.80), while for the folder-based method, the pairwise percent agreement ranged from 86.98 \% to 91.32 \% (Cohen s $\kappa$ = 0.63—0.75) for the task of classifying atypical versus normal mitotic figures, demonstrating high consistency between annotators and comparable performance. Participants rated the tool as highly usable and appreciated the ability to annotate on mobile devices. These results suggest that SWAN can accelerate image annotation while maintaining annotation quality, offering a scalable and user-friendly alternative to conventional workflows. The full codebase of the SWAN project, including analyses, can be found here: https://github.com/DeepMicroscopy/SWAN
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Conference Paper

Sweta Banerjee

Heinz Handels

Katharina Breininger

Thomas Deserno

Andreas Maier

Klaus Maier-Hein

Christoph Palm

Thomas" Tolxdorff

Publikationen

2026

Exploring General-purpose Autonomous Multimodal Agents for Pathology Report Generation

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Weiterführende Links

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Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN

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Weiterführende Links

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Filtering Scheme for Confocal Laser Endomicroscopy (CLE)-video Sequences for Self-supervised Learning

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0

"Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN"

Abstract

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