Publikationen

Autor*in

Jahr der Veröffentlichung

Publikationsformat

Keyword

Forschungsprojekt

Institut/Einrichtung

2026

Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details

Banerjee, S., Bertram, C. A., Weiss, V., Ammeling, J., Conrad, T., Porsche, N., … Aubreville, M. (2026). Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details. Veterinary Pathology. http://doi.org/10.1177/03009858261459452
Suche auf Google Scholar

DOI Verweis
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

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

Fachbereich

Fachbereich 3: Information und Kommunikation
Filter Tags
2026

Journal Article

Sweta Banerjee

Christof Bertram

Viktoria Weiss

Jonas Ammeling

Thomas Conrad

Nils Porsche

Robert Klopfleisch

Christoph Stoblberger

Christopher Kaltenecker

Katharina Breininger

Marc Aubreville
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
Suche auf Google Scholar

DOI Verweis
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

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

Fachbereich

Fachbereich 3: Information und Kommunikation
Filter Tags
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
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation

Banerjee, S., Weiss, V., Donovan, T. A., Fick, R., Conrad, T., Ammeling, J., … Bertram, C. A. (2026). Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation. Machine Learning for Biomedical Imaging, 3, 115–125. http://doi.org/https://doi.org/10.59275/j.melba.2026-6c1g
Suche auf Google Scholar

DOI Verweis
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

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

Fachbereich

Fachbereich 3: Information und Kommunikation
Filter Tags
2026

Journal Article

Sweta Banerjee

Viktoria Weiss

Taryn Donovan

Rutger Fick

Thomas Conrad

Jonas Ammeling

Nils Porsche

Robert Klopfleisch

Christopher Kaltenecker

Katharina Breininger

Marc Aubreville

Christof Bertram
Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations

Bertram, C. A., Weiss, V., Ammeling, J., Schabel, M., Donovan, T. A., Wilm, F., … Aubreville, M. (2026). Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations. Veterinary Pathology, 03009858261457959. http://doi.org/10.1177/03009858261457959

Abstract

Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development and testing of those DL models rely heavily on the availability of high-quality, large-scale data sets. However, creating such data sets is a complex and resource-intensive process, often hindered by challenges such as time constraints, domain variability, and risks of bias in image collection and label creation. This review provides a comprehensive guide to the critical steps in data set creation, including (1) image acquisition, (2) selection of annotation software, and (3) annotation creation. For image acquisition, besides ensuring a sufficiently large number, it is important to address sources of image variability (domain shifts), such as those related to slide preparation and digitization, that could lead to algorithmic errors if not adequately represented in the training data. For annotations, key quality criteria are the 3 “C”s: correctness, consistency, and completeness. For mitigation of annotation bias of a single annotator, this review explores advanced annotation methods (eg, computer-assisted annotations). To support data set creators, a standard operating procedure is provided as supplemental material, summarizing all important considerations for data set creation. Furthermore, this article underscores the importance of open data sets in driving innovation and enhancing reproducibility of DL research. By addressing the challenges and offering practical recommendations, this review aims to advance the creation and availability of high-quality, large-scale data sets, ultimately contributing to the development of generalizable and robust DL models for pathology applications.
Suche auf Google Scholar

DOI Verweis
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Profilbereich

Informatik und Gestaltung

Fachbereich

Fachbereich 3: Information und Kommunikation
Filter Tags
2026

Journal Article

Christof Bertram

Viktoria Weiss

Jonas Ammeling

Maria Schabel

Taryn Donovan

Frauke Wilm

Christian Marzahl

Katharina Breininger

Marc Aubreville

2025

Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma

Banerjee, S., Bertram, C. A., Ammeling, J., Weiss, V., Conrad, T., Klopfleisch, R., … Aubreville, M. (2025). Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma. In Bildverarbeitung für die Medizin 2025 (S. 260–265). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-47422-5_56

Abstract

Automated tumor segmentation of histologic images is crucial for the development of computer-assisted diagnostic workflows aiming at accurate prognostication. We present a dataset of coarse annotations of over 1,000 highresolution breast tumor images from The cancer genome atlas breast invasive carcinoma (TCGA-BRCA) repository, each annotated with binary segmentation masks that delineate coarse tumor areas. Additionally, a subset of 20 images includes fine-grained annotations, providing precise delineation of tumor boundaries beyond the broader outlines used in coarse annotations. Initial evaluations using U-Net and DeepLabv3 models show promising segmentation performance. On a held-out, coarsely annotated test set, U-Net achieved an average intersection over union (IoU) score of 0.795, while DeepLabv3 scored 0.783. On the finely annotated subset of this test set, U-Net reached an average IoU of 0.746, with DeepLabv3 slightly outperforming at 0.765. The public availability of this dataset aims to support research in automated tumor analysis, advancing diagnostic workflows and thereby ultimately improving breast cancer management.
Suche auf Google Scholar

DOI Verweis
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

Banerjee, Sweta

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

CIVU – Center for Interaction, Visualization and Usability

FLAIR - Flensburg Artificial Intelligence Research

Profilbereich

Gesundheit und Life Sciences

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
Filter Tags
2025

Conference Paper

Sweta Banerjee

Christof Bertram

Jonas Ammeling

Viktoria Weiss

Thomas Conrad

Robert Klopfleisch

Christopher Kaltenecker

Katharina Breininger

Marc Aubreville
Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification

Banerjee, S., Weiss, V., Conrad, T., Donovan, T. A., Ammeling, J., Fick, R., … Aubreville, M. (2025). Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification. In Proceedings of the MICCAI Workshop on Computational Pathology (Bd. 316, S. 266–277). PMLR.
Suche auf Google Scholar
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

Banerjee, Sweta

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

Fachbereich

Fachbereich 3: Information und Kommunikation
Filter Tags
2025

Conference Paper

Sweta Banerjee

Viktoria Weiss

Thomas Conrad

Taryn Donovan

Jonas Ammeling

Rutger Fick

Jonas Utz

Robert Klopfleisch

Christopher Kaltenecker

Christof Bertram

Katharina Breininger

Marc Aubreville

Publikationen

2026

Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details

Exportformate

Weiterführende Links

Filter Tags

Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN

Exportformate

Weiterführende Links

Filter Tags

Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation

Exportformate

Weiterführende Links

Filter Tags

Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations

Abstract

Exportformate

Weiterführende Links

Filter Tags

2025

Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma

Abstract

Exportformate

Weiterführende Links

Filter Tags

Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification

Exportformate

Weiterführende Links

Filter Tags