Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
This work addresses prostate cancer diagnosis for pathologists by offering a computer-aided system that localizes Gleason patterns, though it is incremental as it builds on existing U-Net architectures.
The paper tackles the problem of Gleason grading in prostate histology images by using a residual U-Net for semantic segmentation, achieving a pixel-level Cohen's quadratic Kappa of 0.52, which matches previous image-level results while providing detailed localization of cancerous patterns.
Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue via computer-vision algorithms in order to support the physicians' task. The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue according to the full Gleason system. This model outperforms other well-known architectures, and reaches a pixel-level Cohen's quadratic Kappa of 0.52, at the level of previous image-level works in the literature, but providing also a detailed localisation of the patterns.