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näkemys - Universal medical image segmentation
One-Prompt Segmentation: A Versatile Approach for Universal Medical Image Segmentation
One-Prompt Segmentation combines the strengths of one-shot and interactive segmentation methods to enable zero-shot generalization across diverse medical imaging tasks, requiring only a single prompted sample during inference.
Training Diverse Medical Image Segmentation Models for Universal Performance
A single unified model can effectively handle diverse medical image segmentation tasks across various regions, anatomical structures, and imaging modalities by leveraging the synergy and commonality across tasks.
Segment Anything: A Versatile Foundation Model for Medical Image Segmentation
MedSAM, a foundation model designed for bridging the gap in medical image segmentation by enabling accurate and efficient segmentation across a wide spectrum of tasks and modalities.
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