Fig. 1

Study workflow. A Convolutional neural networks for colorectal tissue classification. First, a HE tiles dataset was used to pre-train an untrained VGG-19 network (CNN-0) as the CNN-HE model. Next, an IHC tiles training dataset was used to train the CNN-HE model as the CNN-IHC model with transfer learning. An independent dataset was used to test the tissue tiles classification performance of the CNN-IHC model. B Rough segmentation of IHC (CD3 and CD8) WSI. The CNN-IHC model was used to perform patch-level segmentation of IHC WSI. C Immune cells in the stroma region. The segmented stroma region was mapped on the original WSI, and CD3⁺ and CD8⁺ T-cells in this region were segmented and counted. HE hematoxylin–eosin, IHC immunohistochemical, WSI whole-slide image, ADI adipose, BAC background, DEB debris, LYM lymphocytes, MUC mucus, MUS muscle, NOR normal mucosa, STR stroma, TUM tumor epithelium