Multichannel Contribution Aware Network for Prostate Cancer Grading in Histopathology Images.

Abstract

Gleason grading of prostate histopathology images is widely used by pathologists for diagnosis and prognosis. Spatial characteristics of cell and tissues through staining images is essential for accurate grading of prostate cancer. Although considerable efforts have been made to train grading models, they mainly rely on basic preprocessed images and largely overlook the intricate multiple staining aspects of histopathology images that are crucial for spatial information capture. This article proposes a novel deep learning model for automated prostate cancer grading by integrating several staining characteristics. Image deconvolution is applied to separate the multiple staining channels in the histopathology image, thereby enabling the model to identify effective feature information. A channel and pixel attention-based encoder is designed to extract cell and tissue structure information from multiple staining channel images. We propose a dual-branch decoder, where the classical convolutional neural network branch specializes in local feature extraction and the Transformer branch focuses on global feature extraction, to effectively fuse and refine features from different staining channels. Taking full advantage of the complementarity of multiple staining channels makes the features more compact and discriminative, leading to precise grading. Extensive experiments on relevant public datasets demonstrate the effectiveness and scalability of the proposed model.