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Training Convolutional Networks for Prostate Segmentation with Limited Data
2021
IEEE Access
Multi-zonal segmentation is a critical component of computer-aided diagnostic systems for detecting and staging prostate cancer. Previously, convolutional neural networks such as the U-Net have been used to produce fully automatic multi-zonal prostate segmentation on magnetic resonance images (MRIs) with performance comparable to human experts, but these often require large amounts of manually segmented training data to produce acceptable results. For institutions that have limited amounts of
doi:10.1109/access.2021.3100585
pmid:34527506
pmcid:PMC8438764
fatcat:zyunndtj6bfthk4qudwjf3dxby