SSPT-bpMRI: A Self-supervised Pre-training Scheme for Improving Prostate Cancer Detection and Diagnosis in Bi-parametric MRI*
Author:
Affiliation:
1. The University of Sydney,School of Computer Science, Faculty of Engineering,Sydney,Australia
2. James Cook University,College of Science & Engineering,Cairns,Australia
3. Nepean Hospital,Department of Urology,Kingswood,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10339936/10339939/10340280.pdf?arnumber=10340280
Reference20 articles.
1. Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol);saha,2022
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3. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
4. 3d u-net: learning dense volumetric segmentation from sparse annotation;çiçek;Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016 19th International Conference,2016
5. nnDetection: A Self-configuring Method for Medical Object Detection
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