Learning from Noisy Label Statistics: Detecting High Grade Prostate Cancer in Ultrasound Guided Biopsy

Abstract

The ubiquity of noise is an important issue for building computer-aided diagnosis models for prostate cancer biopsy guidance where the histopathology data is sparse and not finely annotated. We propose a solution to alleviate this challenge as a part of Temporal Enhanced Ultrasound (TeUS)-based prostate cancer biopsy guidance method. Specifically, we embed the prior knowledge from the histopathology as the soft labels in a two-stage model, to leverage the problem of diverse label noise in the ground-truth. We then use this information to accurately detect the grade of cancer and also to estimate the length of cancer in the target. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of model uncertainty that can lead to any possible misguidance during the biopsy procedure. In an in vivo study with 155 patients, we analyze data from 250 suspicious cancer foci obtained during fusion biopsy. We achieve the average area under the curve of 0.84 for cancer grading and mean squared error of 0.12 in the estimation of tumor in biopsy core length.