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Humphreys, Mitchell R

0000-0003-3252-9518

Mayo Clinic

From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J

Citations 1

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI
Cai, Jason , Nakai, Hirotsugu , Kuanar, Shiba , Froemming, Adam T. , Bolan, Candice W. , Kawashima, Akira , Takahashi, Hiroaki , Mynderse, Lance A. , Humphreys, Mitchell R. , Korfiatis, Panagiotis , Bratt, Alex , Takahashi, Naoki , Dora, Chandler D , Humphreys, Mitchell R , Korffiatis, Panagiotis , Rouzrokh, Pouria , Bratt, Alex , Conte, Gian Marco , Erickson, Bradley J , Takahashi, Naoki , Kawashima, Akira , Takahashi, Hiroaki , Dora, Chandler , Rouzrokh, Pouria , Erickson, Bradley J.

Radiology | August 6, 2024 | Volume: 312 | Issue: 2 | Pages: No page available

10.1148/radiol.232635 Get It

The performance of a deep learning model was not different from that of radiologists in the detection of clinically significant prostate cancer at MRI, and gradient-weighted class activation maps localized the tumor.