✨Medal S: Spatio-Textual Prompt Model for Medical Segmentation
📝 Summary:
Medal S is a medical segmentation foundation model using spatio-textual prompts for efficient, high-accuracy multi-class segmentation across diverse modalities. It uniquely aligns volumetric prompts with text embeddings and processes masks in parallel, significantly outperforming prior methods.
🔹 Publication Date: Published on Nov 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13001
• PDF: https://arxiv.org/pdf/2511.13001
• Github: https://github.com/yinghemedical/Medal-S
🔹 Models citing this paper:
• https://huggingface.co/spc819/Medal-S-V1.0
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#MedicalSegmentation #FoundationModels #AI #DeepLearning #ComputerVision
📝 Summary:
Medal S is a medical segmentation foundation model using spatio-textual prompts for efficient, high-accuracy multi-class segmentation across diverse modalities. It uniquely aligns volumetric prompts with text embeddings and processes masks in parallel, significantly outperforming prior methods.
🔹 Publication Date: Published on Nov 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13001
• PDF: https://arxiv.org/pdf/2511.13001
• Github: https://github.com/yinghemedical/Medal-S
🔹 Models citing this paper:
• https://huggingface.co/spc819/Medal-S-V1.0
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MedicalSegmentation #FoundationModels #AI #DeepLearning #ComputerVision