Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images
This addresses domain adaptation for medical image analysis, specifically for pathologists, but appears incremental as it builds on existing methods.
The paper tackles the problem of mitosis cell detection in histopathology images under domain shift by proposing a two-step domain adaptation method using Faster RCNN and CNN with stain augmentation. The result is promising performance on the MIDOG-2021 challenge test data, though no concrete numbers are provided.
We propose a two-step domain shift-invariant mitosis cell detection method based on Faster RCNN and a convolutional neural network (CNN). We generate various domain-shifted versions of existing histopathology images using a stain augmentation technique, enabling our method to effectively learn various stain domains and achieve better generalization. The performance of our method is evaluated on the preliminary test data set of the MIDOG-2021 challenge. The experimental results demonstrate that the proposed mitosis detection method can achieve promising performance for domain-shifted histopathology images.