CoralBay extends DINO self-distillation to 3D CT using hierarchical Swin transformers on concatenated multi-scale features, claiming effective transfer to radiological tasks plus a new public leaderboard.
How well do supervised 3d models transfer to medical imaging tasks?
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
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.
ASSFT combines active test-time sample selection via diversified knowledge divergence and anatomical segmentation difficulty with selective semi-supervised fine-tuning to adapt medical vision foundation models for volumetric segmentation under limited annotation budgets without source data access.
citing papers explorer
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CoralBay: A Self-Supervised CT Foundation Model
CoralBay extends DINO self-distillation to 3D CT using hierarchical Swin transformers on concatenated multi-scale features, claiming effective transfer to radiological tasks plus a new public leaderboard.
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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.
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Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
ASSFT combines active test-time sample selection via diversified knowledge divergence and anatomical segmentation difficulty with selective semi-supervised fine-tuning to adapt medical vision foundation models for volumetric segmentation under limited annotation budgets without source data access.