mlsanityData to 7 Oct 2026

Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

arXiv 2109.00853 ↗First posted 2 Sep 2021cs.CVcited by 14

Not on the map. Why?

Abstract (arXiv)

The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the TIA Centre team to address this challenge. Our approach is based on a hybrid detection model, where mitotic candidates are segmented on stain normalised images, before being refined by a deep learning classifier. Cross-validation on the training images achieved the F1-score of 0.786 and 0.765 on the preliminary test set, demonstrating the generalizability of our model to unseen data from new scanners.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 2 Sep 2021.

Builds on

6 of 10 references.

References · 6

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  2. 2020NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Imagescited by 46
  3. 2014Assessment of algorithms for mitosis detection in breast cancer histopathology imagescited by 16
  4. 2021Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Inputcited by 8
  5. 2018Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentationscited by 6
  6. 2019NuClick: From Clicks in the Nuclei to Nuclear Boundariescited by 5

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 7; 2024: 1; 2025: 1.

14 papers.

Sort:

  1. 2022Mitosis domain generalization in histopathology images -- The MIDOG challengecited by 66
  2. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  3. 2022IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classificationcited by 10
  4. 2022TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challengecited by 7
  5. 2023Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Imagescited by 6
  6. 2023MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysiscited by 6
  7. 2022Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challengecited by 5
  8. 2023SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interactioncited by 4
  9. 2023A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasiacited by 3
  10. 2024Benchmarking Domain Generalization Algorithms in Computational Pathologycited by 3
  11. 2023An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancercited by 1
  12. 2022Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizercited by 0
2 more
  1. 2023Transformer-based Model for Oral Epithelial Dysplasia Segmentationcited by 0
  2. 2025Value bounds and Convergence Analysis for Averages of LRP attributionscited by 0