mlsanityData to 7 Oct 2026

Domain-Robust Mitotic Figure Detection with Style Transfer

arXiv 2109.01124 ↗First posted 2 Sep 2021cs.CVcited by 4

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Abstract (arXiv)

We propose a new training scheme for domain generalization in mitotic figure detection. Mitotic figures show different characteristics for each scanner. We consider each scanner as a 'domain' and the image distribution specified for each domain as 'style'. The goal is to train our network to be robust on scanner types by using various 'style' images. To expand the style variance, we transfer a style of the training image into arbitrary styles, by defining a module based on StarGAN. Our model with the proposed training scheme shows positive performance on MIDOG Preliminary Test-Set containing scanners never seen before.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 4 of its 7 references that are in the map. First posted on arXiv 2 Sep 2021.

Builds on

4 of 7 references.

References · 4

  1. 2019Objects as Pointscited by 1,172
  2. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  3. 2021Probabilistic two-stage detectioncited by 130
  4. 2021Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challengecited by 10

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 1.

4 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. 2022Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figurescited by 6
  4. 2022Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizercited by 0