mlsanityData to 7 Oct 2026

MitoDet: Simple and robust mitosis detection

arXiv 2109.01485 ↗First posted 2 Sep 2021eess.IV cs.CVcited by 3

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Abstract (arXiv)

Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called domain shift. An important source of a domain shift is introduced by different microscopes and their camera systems, which noticeably change the color representation of digitized images. In this method description we present our submitted algorithm for the Mitosis Domain Generalization Challenge, which employs a RetinaNet trained with strong data augmentation and achieves an F1 score of 0.7138 on the preliminary test set.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 2 Sep 2021.

Builds on

11 of 18 references.

References · 11

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  2. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  3. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  4. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  5. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  6. 2020A Simple Semi-Supervised Learning Framework for Object Detectioncited by 282
  7. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225
  8. 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 196
  9. 2018Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networkscited by 57
  10. 2019Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labelscited by 29
  11. 2019Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slidescited by 8

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2022Mitosis domain generalization in histopathology images -- The MIDOG challengecited by 66
  2. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  3. 2022Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizercited by 0