Jonas Ammeling
Researcher
TH Ingolstadt
Publications
2026
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A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis
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Abstract: Investigation of Class Separability within Object Detection Models in Histopathology
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Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation
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Benchmarking Foundation Models for Mitotic Figure Classification
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Cognitive biases in AI–assisted medical decision making: A structured review as a primer for veterinary and human pathology
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Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations
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Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN
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Mitosis Domain Generalization (MIDOG) Challenge 2025 Baselines
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Performance evaluation of deep learning models for image analysis: Considerations for visual assessment and statistical metrics
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Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details
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Reporting transparency in veterinary pathology deep learning: A systematic review of reproducibility-critical details
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Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology
2025
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"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology
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An interdisciplinary perspective on AI-supported decision making in medicine
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Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology
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Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification
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Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma
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Effortless Vision-Language Model Specialization in Histopathology without Annotation
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Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
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Investigation of Class Separability within Object Detection Models in Histopathology
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Is Self-supervision Enough?: Benchmarking Foundation Models Against End-to-end Training for Mitotic Figure Classification
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Mitosis Domain Generalization Challenge 2025
2024
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An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine
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Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning
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Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry
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Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge
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Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
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Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
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Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis
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Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis
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Perceived responsibility in AI-supported medicine
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Perceived responsibility in AI-supported medicine
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Prediction of tumor board procedural recommendations using large language models
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Re-identification from histopathology images
2023
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A comprehensive multi-domain dataset for mitotic figure detection
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Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making
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Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays
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Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images
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Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector
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Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge
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Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge