SAM 3 adapted for medical imaging with parameter-efficient fine-tuning

Researchers have developed a new method to adapt the Segment Anything Model 3 (SAM 3) for generating Internal Target Volumes (ITVs) from 4DCT images. This parameter-efficient fine-tuning approach, utilizing Low-Rank Adaptation (LoRA) and a hard negative mining strategy, significantly improves segmentation accuracy and reduces artifacts in medical imaging. The framework demonstrates high performance with minimal annotated data and can be trained on a single consumer-grade GPU, offering a scalable and data-efficient solution for adaptive radiotherapy. AI

RANK_REASON The cluster describes a research paper detailing a new method for adapting an existing AI model for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

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• 4DCT
• Lora
• Segment Anything Model 3

• paper
• model release

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