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2025

Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology

Bertram, C. A., Schutten, M. ., Ressel, L. ., Breininger, K. ., Webster, J. D., & Aubreville, M. . (2025). Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology. Veterinary Pathology, 62, 615–617. http://doi.org/10.1177/03009858251344320 (Original work published August 2025)
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Aubreville, Marc

Forschungsprojekt

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

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Gesundheit und Life Sciences

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2025

Journal Article

Christof Bertram

Melissa Schutten

Lorenzo Ressel

Katharina Breininger

Joshua Webster

Marc Aubreville
Investigation of Class Separability within Object Detection Models in Histopathology

Ammeling, J. ., Ganz, J. ., Wilm, F. ., Breininger, K. ., & Aubreville, M. . (2025). Investigation of Class Separability within Object Detection Models in Histopathology. IEEE Transactions on Medical Imaging, 1–1. http://doi.org/10.1109/TMI.2025.3560134
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Aubreville, Marc

Forschungsprojekt

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
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2025

Journal Article

Jonas Ammeling

Jonathan Ganz

Frauke Wilm

Katharina Breininger

Marc Aubreville

Feature extraction

Object detection

Measurement

Adaptation models

histopathology

Training

Complexity theory

YOLO

Location awareness

Data mining

object detection

latent space discrimination

2024

Re-identification from histopathology images

Ganz, J. ., Ammeling, J. ., Jabari, S. ., Breininger, K. ., & Aubreville, M. . (2024). Re-identification from histopathology images. Medical Image Analysis, 103335. http://doi.org/10.1016/j.media.2024.103335 (Original work published 2025)
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Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Digitalisierung im Gesundheitswesen

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonathan Ganz

Jonas Ammeling

Samir Jabari

Katharina Breininger

Marc Aubreville
Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

Ganz, J. ., Marzahl, C. ., Ammeling, J. ., Rosbach, E. ., Richter, B. ., Puget, C. ., … Aubreville, M. . (2024). Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis. Scientific Reports, 14, 26273. http://doi.org/10.1038/s41598-024-77244-6

Abstract

Abstract The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&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&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&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&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. 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&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&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.
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DOI Verweis
Exportformate

BibTeX

EndNote X3 XML

EndNote 7 XML

EndNote tagged

RIS
Weiterführende Links
Personenseiten

Aubreville, Marc

Forschungsprojekt

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

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Human-Computer-Interaction

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonathan Ganz

Christian Marzahl

Jonas Ammeling

Emely Rosbach

Barbara Richter

Chloé Puget

Daniela Denk

Elena Demeter

Flaviu Tăbăran

Gabriel Wasinger

Karoline Lipnik

Marco Tecilla

Matthew Valentine

Michael Dark

Niklas Abele

Pompei Bolfa

Ramona Erber

Robert Klopfleisch

Sophie Merz

Taryn Donovan

Samir Jabari

Christof Bertram

Katharina Breininger

Marc Aubreville

2025

Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology

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Investigation of Class Separability within Object Detection Models in Histopathology

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Filter Tags

2024

Re-identification from histopathology images

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Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

Abstract

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