  Prostate cancer is the most common cancer in men worldwide and the second
leading cause of cancer death in the United States. One of the prognostic
features in prostate cancer is the Gleason grading of histopathology images.
The Gleason grade is assigned based on tumor architecture on Hematoxylin and
Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process
is time-consuming and has known interobserver variability. In the past few
years, deep learning algorithms have been used to analyze histopathology
images, delivering promising results for grading prostate cancer. However, most
of the algorithms rely on the fully annotated datasets which are expensive to
generate. In this work, we proposed a novel weakly-supervised algorithm to
classify prostate cancer grades. The proposed algorithm consists of three
steps: (1) extracting discriminative areas in a histopathology image by
employing the Multiple Instance Learning (MIL) algorithm based on Transformers,
(2) representing the image by constructing a graph using the discriminative
patches, and (3) classifying the image into its Gleason grades by developing a
Graph Convolutional Neural Network (GCN) based on the gated attention
mechanism. We evaluated our algorithm using publicly available datasets,
including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross
validated the algorithm on an independent dataset. Results show that the
proposed model achieved state-of-the-art performance in the Gleason grading
task in terms of accuracy, F1 score, and cohen-kappa. The code is available at
https://github.com/NabaviLab/Prostate-Cancer.
