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

TL;DR

This paper introduces a domain adaptive cascade R-CNN approach for mitosis detection in histopathology images, achieving notable generalization performance in the MIDOG challenge.

Contribution

It adapts existing detection architectures with comprehensive data augmentation for improved domain generalization in mitosis detection.

Findings

Achieved an F1 score of 0.7500 on MIDOG test set.

Demonstrated effective domain adaptation for histopathology image analysis.

Showcased competitive performance in a MICCAI challenge.

Abstract

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.