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The availability of large scale data with high quality ground truth labels isa challenge when developing supervised machine learning solutions forhealthcare domain. Although, the amount of digital data in clinical workflowsis increasing, most of this data is distributed on clinical sites and protectedto ensure patient privacy. Radiological readings and dealing with large-scaleclinical data puts a significant burden on the available resources, and this iswhere machine learning and artificial intelligence play a pivotal role.Magnetic Resonance Imaging (MRI) for musculoskeletal (MSK) diagnosis is oneexample where the scans have a wealth of information, but require a significantamount of time for reading and labeling. Self-supervised learning (SSL) can bea solution for handling the lack of availability of ground truth labels, butgenerally requires a large amount of training data during the pretrainingstage. Herein, we propose a slice-based self-supervised deep learning framework(SB-SSL), a novel slice-based paradigm for classifying abnormality using kneeMRI scans. We show that for a limited number of cases (<1000), our proposedframework is capable to identify anterior cruciate ligament tear with anaccuracy of 89.17% and an AUC of 0.954, outperforming state-of-the-art withoutusage of external data during pretraining. This demonstrates that our proposedframework is suited for SSL in the limited data regime.

SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI