Dual-Stage Domain Adaptive Mitosis Detection for Histopathology Images

Histopathology images for mitosis detection vary in appearance due to the non-standard method of preparing the tissues as well as differences in scanner hardware. This makes automatic machine learning based mitosis detection very challenging because of domain shift between the training and testing datasets. In this paper, we propose a method of addressing this domain shift problem by using a two-stage domain adaptive neural network. In the first stage, we use domain adaptive Mask R-CNN to generate masks for mitotic regions. Thus generated masks are used by a second domain adaptive convolutional neural network to perform finer mitosis detection. Our method achieved state-of-the-art performance on both ICPR 2012 and 2014 datasets. We demonstrate that using a domain agnostic approach achieves better generalization and mitosis cell localization for the trained models.