TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound

Mohamed Harmanani, Paul F. R. Wilson, Minh Nguyen Nhat To, Mahdi Gilany, Amoon Jamzad, Fahimeh Fooladgar, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

Abstract: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. There is high heterogeneity in tissue appearance, significant class imbalance in favor of benign examples, and scarcity in the number and quality of ground truth annotations available to train models. Failure to address even a single one of these problems can result in unacceptable clinical outcomes.

External IDs:dblp:journals/cars/HarmananiWTGJFWAM25