SMIT, which combines masked image modeling with self-distillation, delivers the highest segmentation accuracy, fastest convergence, and best few-shot performance across nine CT and MRI tasks compared to contrastive and rotation-based SSL methods.
Self-supervised learning improves robustness of deep learning lung tumor segmentation models to ct imaging differences.Medical Physics, 52(3):1573–1588, 2025
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Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks
SMIT, which combines masked image modeling with self-distillation, delivers the highest segmentation accuracy, fastest convergence, and best few-shot performance across nine CT and MRI tasks compared to contrastive and rotation-based SSL methods.