A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate
David Winkel(University of Basel), Daniel T. Boll(University Hospital of Basel), Heinrich von Busch(Siemens Healthineers (Germany)), Ali Kamen(Siemens Healthcare (United States)), Moon Hyung Choi(The Catholic University of Korea Seoul St. Mary's Hospital), Jonathan A. Disselhorst(Siemens (Switzerland)), Ivan Shabunin, Angela Tong(Brigham and Women's Hospital), Pengyi Xing, Henkjan Huisman(Radboud University Nijmegen), Dieter Szolar(Geriatrische Gesundheitszentren), Robert Grimm(Siemens Healthineers (Germany)), Bin Lou(Fudan University), Tobias Penzkofer(Screen), Alejandro Rodríguez‐Ruiz, Dorin Comaniciu(Siemens (United States))
Cited by 110
Related Papers
TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images
|Radiology Artificial Intelligence|2023|1.3k
Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists
|JNCI Journal of the National Cancer Institute|2018|674
Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
|Radiology|2018|601
Sodium–Glucose Cotransporter-2 Inhibitors and the Risk for Severe Urinary Tract Infections
|Annals of Internal Medicine|2019|215