Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge
arXiv:2109.00965v24 citations
Originality Synthesis-oriented
AI Analysis
This work addresses domain generalization for mitosis detection in medical imaging, but it is incremental as it adapts existing methods.
The paper tackled mitosis detection in digital histopathology images by adapting a cascade R-CNN method with data augmentation, achieving an F1 score of 0.7500 on a preliminary test set in the MIDOG Challenge.
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.