Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images

• URL: http://arxiv.org/abs/2212.12844v1
• Date: Sun, 25 Dec 2022 03:07:52 GMT
• Title: Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images
• Authors: Mohammad Mahdi Behzadi, Mohammad Madani, Hanzhang Wang, Jun Bai, Ankit Bhardwaj, Anna Tarakanova, Harold Yamase, Ga Hie Nam, Sheida Nabavi
• Abstract summary: We propose a weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: extracting discriminative areas in a histopathology image, representing the image, and classifying the image into its Gleason grades. 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.
• Score: 2.547129771651519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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