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.
@article{arxiv.1702.05931,
title = {The importance of stain normalization in colorectal tissue classification with convolutional networks},
author = {Francesco Ciompi and Oscar Geessink and Babak Ehteshami Bejnordi and Gabriel Silva de Souza and Alexi Baidoshvili and Geert Litjens and Bram van Ginneken and Iris Nagtegaal and Jeroen van der Laak},
journal= {arXiv preprint arXiv:1702.05931},
year = {2017}
}
Comments
Published in Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) 2017