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Bridging the gap between prostate radiology and pathology through machine learning
[article]
2021
arXiv
pre-print
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model
arXiv:2112.02164v1
fatcat:qdrpiaxhdfg2vdczc2zfofpxqa