HMARNET—A Hierarchical Multi‐Attention Residual Network for Gleason scoring of prostate cancer
R. Karthik,
R. Menaka,
M. V. Siddharth
et al.
Abstract: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 fe… Show more
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