LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations

Minh Nguyen Nhat To, Fahimeh Fooladgar, Paul F. R. Wilson, Mohamed Harmanani, Mahdi Gilany, Samira Sojoudi, Amoon Jamzad, Silvia D. Chang, Peter C. Black, Parvin Mousavi, Purang Abolmaesumi

Abstract: The standard of care for prostate cancer (PCa) diagnosis is the histopathological analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy. Models built with deep neural networks (DNNs) hold the potential for direct PCa detection from TRUS, which allows targeted biopsy and subsequently enhances outcomes. Yet, there are ongoing challenges with training robust models, stemming from issues such as noisy labels, out-of-distribution (OOD) data, and limited labeled data.