# Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)

arXiv: https://arxiv.org/abs/2501.04467 · HTML: https://mlsanity.com/paper/2501.04467/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [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)
- [Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge](https://mlsanity.com/paper/2309.15589/) (2023)
- [Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector](https://mlsanity.com/paper/2212.05900/) (2022)

## Built on by (most cited first, 17 of 17)

- [Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation](https://mlsanity.com/paper/2506.21444/) (2025)
- [Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification](https://mlsanity.com/paper/2508.21041/) (2025)
- [MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping](https://mlsanity.com/paper/2509.02586/) (2025)
- [Mix, Align, Distil: Reliable Cross-Domain Atypical Mitosis Classification](https://mlsanity.com/paper/2508.20745/) (2025)
- [Classifying Mitotic Figures in the MIDOG25 Challenge with Deep Ensemble Learning and Rule Based Refinement](https://mlsanity.com/paper/2508.20919/) (2025)
- [Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification](https://mlsanity.com/paper/2509.02591/) (2025)
- [Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025](https://mlsanity.com/paper/2509.02597/) (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)
- [Foundation Model-Driven Classification of Atypical Mitotic Figures with Domain-Aware Training Strategies](https://mlsanity.com/paper/2509.02601/) (2025)
- [Is Synthetic Image Augmentation Useful for Imbalanced Classification Problems? Case-Study on the MIDOG2025 Atypical Cell Detection Competition](https://mlsanity.com/paper/2509.02612/) (2025)
- [Adaptive Learning Strategies for Mitotic Figure Classification in MIDOG2025 Challenge](https://mlsanity.com/paper/2509.02640/) (2025)
- [Teacher-Student Model for Detecting and Classifying Mitosis in the MIDOG 2025 Challenge](https://mlsanity.com/paper/2509.03614/) (2025)
- [MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances](https://mlsanity.com/paper/2509.10502/) (2025)
- [Parameter-efficient fine-tuning (PEFT) of Vision Foundation Models for Atypical Mitotic Figure Classification](https://mlsanity.com/paper/2509.16935/) (2025)
- [Robust Atypical Mitosis Classification with DenseNet121: Stain-Aware Augmentation and Hybrid Loss for Domain Generalization](https://mlsanity.com/paper/2510.22630/) (2025)
- [SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces](https://mlsanity.com/paper/2511.08271/) (2025)
- [MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis](https://mlsanity.com/paper/2609.24736/) (2026)

## Abstract (arXiv)

Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4%) and 2,888 normal MFs (77.6%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance. We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split.