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Self-supervised and supervised learning in medical imaging classification: addressing the hidden bias of workflow design.
Andrea Espis, Chiara Marzi, Stefano Diciotti
Published: 202510.1109/EMBC58623.2025.11254853
Abstract
The rise of self-supervised learning (SSL) in medical imaging holds immense potential, particularly for leveraging unlabeled data and achieving surprising performance in scenarios with limited annotations. Our study shows that comparisons with tradit…
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