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Abstract #4056

Self-Supervised Deep Learning for Knee MRI Segmentation using Limited Labeled Training Datasets

Jeffrey Dominic1, Arjun Desai1, Andrew Schmidt1, Elka Rubin1, Garry Gold1, Brian Hargreaves1, and Akshay Chaudhari1
1Stanford University, Stanford, CA, United States

Deep learning (DL)-based approaches have shown promise for automating medical image segmentation with high efficacy. However, current state-of-the-art DL supervised methods require large extents of labeled training images, which are difficult to curate at scale. In this work, we propose a self-supervised training scheme to reduce dependence on labeled data by pretraining networks in an unsupervised manner. We show that our method can improve segmentation performance, especially in the context of very limited data scenarios (only 10-25% scans available) and can achieve or surpass the accuracy of state-of-the-art supervised networks with approximately 50% fewer labeled scans.

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