An end-to-end cells detection approach for colon cancer histology images
Xingguo Zhang, Guoyue Chen, Kazuki Saruta, Yuki Terata · 2018
The qualitative and quantitative analysis of different types of histopathology images of cancerous tissue can not only help us in better understanding of tumor but also explore various options for cancer treatment. However, it is still a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on image detection in various tasks. In this paper, we investigate issues involving Faster R-CNN for construction of end-to-end colorectal adenocarcinoma images analysis system. We experimented with different types of network for extract features, and analyzed the impact of time and accuracy. In addition, we optimize the various stages of the network training process. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated cells belonging to four different classes. Our results presenting competitive accuracy and acceptable running time. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images. Code and dataset will be made publicly available.