mlsanityData to 14 Oct 2026

Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge

arXiv 2109.00965 ↗First posted 1 Sep 2021eess.IV cs.CVcited by 3

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Abstract (arXiv)

We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

4 of 13 references.

References · 4

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  2. 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
  3. 2018Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challengecited by 61
  4. 2019Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentationcited by 4

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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 67
  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