Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images

TL;DR

This paper introduces a two-step domain adaptation method using Faster RCNN and CNN for mitosis cell detection in histopathology images, enhancing generalization across stain variations.

Contribution

The paper presents a novel stain augmentation technique combined with a two-step domain adaptation approach for improved mitosis detection in histopathology images.

Findings

Achieves promising performance on MIDOG-2021 test data

Effectively learns from various stain domains

Improves generalization in domain-shifted images

Abstract

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.