UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation

Name: UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Uploaded: 2026-10-03T12:22:19.392281+00:00
Duration: 336 s
Description: UniMedSeg creates a single Transformer model to handle diverse medical image segmentation tasks, including 2D and 3D images, dense masks, geometric prompts, and language instructions. It achieves this by mapping all these different inputs into one shared sequence space. This allows the model to learn from various data types jointly without needing separate fine-tuning for each specific task.

UniMedSeg creates a single Transformer model to handle diverse medical image segmentation tasks, including 2D and 3D images, dense masks, geometric prompts, and language instructions. It achieves this by mapping all these different inputs into one shared sequence space. This allows the model to learn from various data types jointly without needing separate fine-tuning for each specific task.

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