Tyler Ward

Joint training of self-supervised and supervised objectives can significantly boost efficiency and robustness in low-label settings, challenging the dominance of traditional pretraining-finetuning approaches.

SARFA achieves superior medical image segmentation by aligning radiomic features, outperforming traditional methods in ambiguous cases where expert consensus is often lacking.