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CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis.

, , , , , , , , , , , , and . MICCAI (2), volume 12262 of Lecture Notes in Computer Science, page 315-325. Springer, (2020)

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Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework., , , , , , , , , and 3 other author(s). Medical Image Anal., (2022)Intensity normalization of prostate MRIs using conditional generative adversarial networks for cancer detection., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis., , , , , , , , , and 3 other author(s). MICCAI (2), volume 12262 of Lecture Notes in Computer Science, page 315-325. Springer, (2020)Bridging the gap between prostate radiology and pathology through machine learning., , , , , , , , , and 4 other author(s). CoRR, (2021)Clinically significant prostate cancer detection on MRI with self-supervised learning using image context restoration., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)ProGNet: prostate gland segmentation on MRI with deep learning., , , , , , , , , and 1 other author(s). Medical Imaging: Image Processing, volume 11596 of SPIE Proceedings, SPIE, (2021)