Characterizing The Uncertainty Of Label Noise In Systematic Ultrasound-Guided Prostate Biopsy

Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using a systematic and nontargeted approach are the high rate of false negatives and not being patient specific. Intraprostatic pathology information of individuals is not available during the biopsy procedure. Even after histopathology analysis of the biopsy cores, the report only represents a statistical distribution of cancer within the core. Labeling the data based on these noisy labels results in challenges for network training, where networks inevitably overfit to noisy data. To overcome this problem, we argue that it is critical to build a clean dataset. In this paper, we address the challenges associated with using statistical labels and alleviate this issue by taking advantage of confident learning to estimate uncertainty in the data label. Next, we find the label error, clean the labels, and evaluate the clean data by comparing it using a metric based on the involvement of cancer in core.