A Generalized Densely Connected Encoder-Decoder Network for epithelial and stromal regions segmentation in histopathological images

Identification of epithelial and stromal regions by a computerized system is quite challenging due to their irregular shape and size in histopathological images. Nowadays, although convolutional neural networks(CNNs) have significantly push forward this field, the typical use of CNNs in histopathological images is to classify an image block into a corresponding category. It fails to enable a better adherence of class boundaries due to the trade-off between networks' localization accuracy and input context. Larger blocks require more pooling layers that reduce localization accuracy, while tiny blocks allow networks to see only a small amount of context. So in this paper, we propose a generalized densely connected encoder-decoder network to deal with this problem. The main idea behind our network is captured in dense skip connections to compensate for resolution loss induced by pooling layers. So our network can have a large amount of context as input without losing localization accuracy. Finally, we show that the proposed network can outperform U-Net on Stanford Tissue Microarray Database without any further post-processing module or pretraining. Moreover, due to smart construction of the model, our approach has much fewer parameters than currently published best entries for this dataset. This means our approach is much faster than currently published entries. Segmentation of a $512\times 512$ image only takes 34ms on an Nvidia Tesla P100 GPU.