Converting 3D MRI volumes into action-conditioned 2D slice navigation sequences offers a complementary self-supervised pretraining signal for learning anatomical and spatial representations.
Revisiting mae pre-training for 3d medical image segmentation
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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.
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3D MRI Image Pretraining via Controllable 2D Slice Navigation Task
Converting 3D MRI volumes into action-conditioned 2D slice navigation sequences offers a complementary self-supervised pretraining signal for learning anatomical and spatial representations.
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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.