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Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI
[article]
2019
arXiv
pre-print
Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continues to gain clinical acceptance as the preferred imaging technique for non-invasive detection and grading of PCa. However, the machine learning-based diagnosis systems for PCa are often constrained by the limited access to accurate lesion ground truth annotations for training. The performance of the machine learning
arXiv:1910.02185v1
fatcat:av53gmg23rhiddhskvph64k5pm