# Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge

arXiv: https://arxiv.org/abs/2309.15589 · HTML: https://mlsanity.com/paper/2309.15589/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 85 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks](https://mlsanity.com/paper/1808.05896/) (2018)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Why is the winner the best?](https://mlsanity.com/paper/2303.17719/) (2023)
- [Assessment of algorithms for mitosis detection in breast cancer histopathology images](https://mlsanity.com/paper/1411.5825/) (2014)
- [EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control](https://mlsanity.com/paper/2004.14595/) (2020)
- [Quantifying the Scanner-Induced Domain Gap in Mitosis Detection](https://mlsanity.com/paper/2103.16515/) (2021)
- [Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels](https://mlsanity.com/paper/2007.05351/) (2020)
- [Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input](https://mlsanity.com/paper/2108.13368/) (2021)
- [Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge](https://mlsanity.com/paper/2208.12437/) (2022)
- [Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset](https://mlsanity.com/paper/2301.01079/) (2023)
- [Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge](https://mlsanity.com/paper/2208.13902/) (2022)

## Built on by (most cited first, 25 of 25)

- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology](https://mlsanity.com/paper/2412.12077/) (2024)
- [Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)](https://mlsanity.com/paper/2501.04467/) (2025)
- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation](https://mlsanity.com/paper/2506.21444/) (2025)
- [OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides](https://mlsanity.com/paper/2407.12773/) (2024)
- [Benchmarking Domain Generalization Algorithms in Computational Pathology](https://mlsanity.com/paper/2409.17063/) (2024)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Benchmarking Foundation Models for Mitotic Figure Classification](https://mlsanity.com/paper/2508.04441/) (2025)
- [Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification](https://mlsanity.com/paper/2508.21041/) (2025)
- [Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment](https://mlsanity.com/paper/2511.05150/) (2025)
- [On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images](https://mlsanity.com/paper/2406.19899/) (2024)
- [Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas](https://mlsanity.com/paper/2501.15743/) (2025)
- [A Unified Low-level Foundation Model for Enhancing Pathology Image Quality](https://mlsanity.com/paper/2509.01071/) (2025)
- [A Deployment-Friendly Foundational Framework for Efficient Computational Pathology](https://mlsanity.com/paper/2602.14010/) (2026)
- [Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models](https://mlsanity.com/paper/2409.11752/) (2024)
- [Cross-Task Pretraining for Cross-Organ Cross-Scanner Adenocarcinoma Segmentation](https://mlsanity.com/paper/2410.07124/) (2024)
- [Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification](https://mlsanity.com/paper/2412.06365/) (2024)
- [Automated Classification of Normal and Atypical Mitotic Figures Using ConvNeXt V2: MIDOG 2025 Track 2](https://mlsanity.com/paper/2508.18831/) (2025)
- [Mix, Align, Distil: Reliable Cross-Domain Atypical Mitosis Classification](https://mlsanity.com/paper/2508.20745/) (2025)
- [Sequential Hard Mining: a data-centric approach for Mitosis Detection](https://mlsanity.com/paper/2509.02588/) (2025)
- [Team Westwood Solution for MIDOG 2025 Challenge: An Ensemble-CNN-Based Approach For Mitosis Detection And Classification](https://mlsanity.com/paper/2509.02600/) (2025)
- [Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement](https://mlsanity.com/paper/2602.12317/) (2026)
- [Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures](https://mlsanity.com/paper/2607.28007/) (2026)
- [MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis](https://mlsanity.com/paper/2609.24736/) (2026)

## Abstract (arXiv)

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert consensus and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, but with only minor changes in the order of participants in the ranking.