Semi-supervised learning from coarse histopathology labels

Ultrasound imaging is commonly used to guide sampling the prostate tissue in transrectal biopsies, followed by detection of cancer through histopathological analysis and coarse labeling (cancer or benign) of sampled tissue. The biopsy procedure is currently blind to the underlying tissue microstructure. Ideally, the procedure should be improved by developing machine learning solutions that can identify the presence of cancer in ultrasound images to guide the biopsy procedure. Training a fully supervised learning model for ultrasound using coarse histopathology labels suffers from weakly annotated data. Weak labels of biopsy samples also introduce label noise for each image pixel, making the training process even more challenging, as deep learning models are prone to overfit to noisy samples which negatively affects their generalization. To address this challenge, we propose a semi-supervised framework for learning with noisy labels. To prepare the labeled and unlabeled sets from weakly annotated data, we leverage a two-component mixture model to cluster the training data into clean and noisy label samples based on their loss values. To prevent the model from overfitting and memorizing noisy samples, we propose two models trained in a co-teaching pipeline. Then, during the semi-supervised training phase, we utilize the well-known MixMatch algorithm which incorporates consistency regularization, entropy minimization, and the Mixup regularization as well as the cross-entropy loss function for unlabeled and labeled sets, respectively. We evaluate the proposed framework with prostate ultrasound data obtained from 71 subjects, while sampling 264 biopsy cores. We achieve balanced accuracy, sensitivity, and specificity of 78.6%, 80.0%, and 77.1%, respectively. In a detailed comparison study, we demonstrate that our proposed framework outperforms the fully supervised method with state-of-the-art robust loss functions to deal with label noise.