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Deep learning-based analysis of high-frequency, high-resolutionmicro-ultrasound data shows great promise for prostate cancer detection.Previous approaches to analysis of ultrasound data largely follow a supervisedlearning paradigm. Ground truth labels for ultrasound images used for trainingdeep networks often include coarse annotations generated from thehistopathological analysis of tissue samples obtained via biopsy. This createsinherent limitations on the availability and quality of labeled data, posingmajor challenges to the success of supervised learning methods. On the otherhand, unlabeled prostate ultrasound data are more abundant. In this work, wesuccessfully apply self-supervised representation learning to micro-ultrasounddata. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained intwo clinical centres, we demonstrate that feature representations learnt withthis method can be used to classify cancer from non-cancer tissue, obtaining anAUROC score of 91% on an independent test set. To the best of our knowledge,this is the first successful end-to-end self-supervised learning approach forprostate cancer detection using ultrasound data. Our method outperformsbaseline supervised learning approaches, generalizes well between differentdata centers, and scale well in performance as more unlabeled data are added,making it a promising approach for future research using large volumes ofunlabeled data.

Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound