HMARNET—A Hierarchical Multi‐Attention Residual Network for Gleason scoring of prostate cancer

Manual delineation of prostate cancer (PCa) from whole slide images (WSIs) demands requires pathologists with adequate domain knowledge. This process is generally strenuous and may be subjected to poor inter‐pathologist reproducibility. Accurate Gleason scoring is an important step in the computer‐a...

Full description

Saved in:
Bibliographic Details
Published in:International journal of imaging systems and technology 2024-01, Vol.34 (1), p.n/a
Main Authors: Karthik, R., Menaka, R., Siddharth, M. V., Hussain, Sameeha, Siddharth, P., Won, Daehan
Format: Article
Language:English
Subjects:
Artificial neural networks
attention
Datasets
deep learning
Feature maps
histopathology
Lightweight
Medical imaging
Optimization
Prostate cancer
TCGA‐PRAD
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Manual delineation of prostate cancer (PCa) from whole slide images (WSIs) demands requires pathologists with adequate domain knowledge. This process is generally strenuous and may be subjected to poor inter‐pathologist reproducibility. Accurate Gleason scoring is an important step in the computer‐aided diagnosis of PCa. This work proposes a novel lightweight convolutional neural networks (CNN) to extract significant hierarchical features from the histopathology images. It learns meticulous attention‐guided feature representations through the convolutional layers for precise scoring of Gleason grades. The Hierarchical Multi‐Attention Residual (HMAR) block extracts attention‐guided features and fuses the resulting feature maps from multiple levels with a reduced number of trainable parameters. We have also proposed a new lightweight channel‐attention module, enhanced channel attention (ECA) to extract inter‐channel features from different receptive fields. With the presence of different attention mechanisms, the network learns to focus on more significant features rather than unnecessary ones. Additionally, mixed FocalOHEM loss is proposed to optimize the CNN and efficiently minimize the error. While the focal loss helps to address the class imbalance present in the TCGA‐PRAD dataset, the online hard example mining (OHEM) loss focuses on optimizing hard negative samples. It achieved an accuracy of 89.19% with a Kappa score of 86% on the TCGA‐PRAD dataset.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22976