Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation
Merged summary
TL;DR - MSSA is a training-free test-time adaptation framework for VLM-based medical image segmentation that uses reliable predictions stored in online memory instead of updating model parameters. It improves robustness under distribution shifts while preserving pretrained features.
- Filters noisy image-text predictions to build a stable online memory of semantic priors.
- Aligns target images with structurally relevant memory samples and their predictions.
- Avoids instability and feature degradation associated with update-based adaptation.
- Reports gains of up to 12.2% DSC and 11.7% mIoU across medical segmentation benchmarks.
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Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation
TL;DR - MSSA is a training-free test-time adaptation framework for VLM-based medical image segmentation that uses reliable predictions stored in online memory instead of updating model parameters. It improves robustness under distribution shifts while preserving pretrained features.
- Filters noisy image-text predictions to build a stable online memory of semantic priors.
- Aligns target images with structurally relevant memory samples and their predictions.
- Avoids instability and feature degradation associated with update-based adaptation.
- Reports gains of up to 12.2% DSC and 11.7% mIoU across medical segmentation benchmarks.