activity
20172026
most citedQuantifying the Scanner-Induced Domain Gap in Mitosis Detection

19 citations · 45 across the 19 of their papers we have counts for

collaborators

32 papers

cs.CV2026

Performance evaluation of deep learning models for image analysis: considerations for visual control and statistical metrics

Christof A. Bertram, Jonas Ammeling, Alexander Bartel +2

Deep learning-based automated image analysis (DL-AIA) has been shown to outperform trained pathologists in tasks related to feature quantification. Related to these capacities the…

cs.HC20265 cited

Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology

Emely Rosbach, Jonas Ammeling, Jonathan Ganz +4

Artificial intelligence (AI)-driven decision support systems can improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts…

cs.HC2026

Exploring General-Purpose Autonomous Multimodal Agents for Pathology Report Generation

Marc Aubreville, Taryn A. Donovan, Christof A. Bertram

Recent advances in agentic artificial intelligence, i.e. systems capable of autonomous perception, reasoning, and tool use, offer new opportunities for digital pathology. In this p…

cs.CV2025

Decomposition Sampling for Efficient Region Annotations in Active Learning

Jingna Qiu, Frauke Wilm, Mathias Öttl +5

Active learning improves annotation efficiency by selecting the most informative samples for annotation and model training. While most prior work has focused on selecting informati…

cs.CV2025

Dataset creation for supervised deep learning-based analysis of microscopic images -- review of important considerations and recommendations

Christof A. Bertram, Viktoria Weiss, Jonas Ammeling +6

Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development a…

cs.CV2025

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

Sweta Banerjee, Timo Gosch, Sara Hester +11

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 figu…