Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network

Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network

The Gleason score (GS) is essential in categorizing prostate cancer risk using biopsy. The aim of this study was to propose a two-class GS classification (< and ≥GS 7) methodology using a three-dimensional convolutional neural network with semantic segmentation to predict GS non-invasively using...

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Bibliographic Details
Main Authors: Takaaki Yoshimura, Keisuke Manabe, Hiroyuki Sugimori
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Applied Sciences
Subjects:
gleason score
classification
prostate cancer
semantic segmentation
three-dimensional convolutional neural network (3D-CNN)
Online Access:https://www.mdpi.com/2076-3417/13/14/8028

Internet

https://www.mdpi.com/2076-3417/13/14/8028

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