The importance of stain normalization in colorectal tissue classification with convolutional networks
This work addresses the need for accurate and reproducible imaging biomarkers in colorectal cancer analysis, but it appears incremental as it focuses on applying existing methods to a specific domain.
The paper tackled the problem of colorectal cancer tissue classification in histopathology images by investigating the importance of stain normalization and using convolutional networks, achieving performance reported on a cohort of rectal cancer samples and an independent public dataset.
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H&E images.