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Found 6 papers, 5 papers with code
SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts
Specifically, based on SAM, SAMCT is further equipped with a U-shaped CNN image encoder, a cross-branch interaction module, and a task-indicator prompt encoder.
SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation
Segment anything model (SAM), an eminent universal image segmentation model, has recently gathered considerable attention within the domain of medical image segmentation.
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Following CSA, 2D convolution is utilized for feature refinement through CFFN.
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
To our best knowledge, this is the first work on transformer pruning for medical image analysis tasks.
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Specifically, to fully explore the benefits of transformers in long-range dependency establishment, a cross-scale global transformer (CGT) module is introduced to jointly utilize multiple small-scale feature maps for richer global features with lower computational complexity.
Skull-RCNN: A CNN-based network for the skull fracture detection
Guiding by the morphological features of the skull, a skeleton-based region proposal method is proposed to make candidate boxes more concentrated in key regions and reduce invalid boxes.
