mlsanityData to 14 Oct 2026

Mitosis domain generalization in histopathology images -- The MIDOG challenge

arXiv 2204.03742 ↗First posted 6 Apr 2022eess.IV cs.CV physics.med-phcited by 67

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Abstract (arXiv)

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance.

Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). Builds on 31 of its 75 references that are in the map. First posted on arXiv 6 Apr 2022.

Builds on

31 of 75 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  11. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
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19 more
  1. 2018Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challengecited by 61
  2. 2020NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Imagescited by 46
  3. 2020Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysiscited by 32
  4. 2014Assessment of algorithms for mitosis detection in breast cancer histopathology imagescited by 16
  5. 2020EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version controlcited by 16
  6. 2021Quantifying the Scanner-Induced Domain Gap in Mitosis Detectioncited by 16
  7. 2021Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challengecited by 14
  8. 2020Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labelscited by 12
  9. 2019Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slidescited by 8
  10. 2021Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Inputcited by 8
  11. 2021Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challengecited by 6
  12. 2021Sk-Unet Model with Fourier Domain for Mitosis Detectioncited by 6
  13. 2021Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology imagescited by 5
  14. 2021Domain-Robust Mitotic Figure Detection with Style Transfercited by 4
  15. 2021Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Imagescited by 3
  16. 2021Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challengecited by 3
  17. 2021MitoDet: Simple and robust mitosis detectioncited by 3
  18. 2021Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challengecited by 2
  19. 2021Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detectioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 8; 2023: 26; 2024: 8; 2025: 15; 2026: 10.

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  1. 2022Metrics reloaded: Recommendations for image analysis validationcited by 195
  2. 2023Artificial Intelligence for Digital and Computational Pathologycited by 108
  3. 2022Deep Learning-enabled Virtual Histological Staining of Biological Samplescited by 42
  4. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  5. 2023Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challengecited by 25
  6. 2025Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)cited by 17
  7. 2022Artifact-Based Domain Generalization of Skin Lesion Modelscited by 11
  8. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 11
  9. 2023Domain-specific optimization and diverse evaluation of self-supervised models for histopathologycited by 11
  10. 2022TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challengecited by 8
  11. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  12. 2024Tissue Concepts: supervised foundation models in computational pathologycited by 8
38 more
  1. 2025Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluationcited by 7
  2. 2022Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figurescited by 6
  3. 2023VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathologycited by 6
  4. 2023Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathologycited by 6
  5. 2023MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysiscited by 6
  6. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  7. 2023Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Datasetcited by 5
  8. 2023Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Datasetcited by 3
  9. 2023A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasiacited by 3
  10. 2023Generative Adversarial Networks for Stain Normalisation in Histopathologycited by 3
  11. 2023Improving mitosis detection on histopathology images using large vision-language modelscited by 3
  12. 2024Rethinking U-net Skip Connections for Biomedical Image Segmentationcited by 3
  13. 2024Re-identification from histopathology imagescited by 3
  14. 2025SCORPION: Addressing Scanner-Induced Variability in Histopathologycited by 3
  15. 2022Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detectorcited by 2
  16. 2023Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathologycited by 2
  17. 2025Benchmarking Foundation Models for Mitotic Figure Classificationcited by 2
  18. 2025Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classificationcited by 2
  19. 2022Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challengecited by 1
  20. 2023Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Modelcited by 1
  21. 2023Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalizationcited by 1
  22. 2023An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancercited by 1
  23. 2024On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Imagescited by 1
  24. 2024Leveraging image captions for selective whole slide image annotationcited by 1
  25. 2025Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challengecited by 1
  26. 2026Cross-Fusion Distance: A Novel Metric for Measuring Fusion and Separability Between Data Groups in Representation Spacecited by 1
  27. 2026FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathologycited by 1
  28. 2022Multi tasks RetinaNet for mitosis detectioncited by 0
  29. 2023Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slidescited by 0
  30. 2023Adaptive Domain Generalization for Digital Pathology Imagescited by 0
  31. 2023The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private datacited by 0
  32. 2023Rethinking Mitosis Detection: Towards Diverse Data and Feature Representationcited by 0
  33. 2023DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentationcited by 0
  34. 2023Transformer-based Model for Oral Epithelial Dysplasia Segmentationcited by 0
  35. 2023Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentationcited by 0
  36. 2023Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standardcited by 0
  37. 2024Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slidescited by 0
  38. 2024Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Modelscited by 0

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