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

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.CV →

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

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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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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Changwoo Song ·

    Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images

    arXiv:2606.15604v1 Announce Type: new Abstract: Four-dimensional computed tomography (4DCT) captures the full respiratory cycle of thoracic anatomy, yet current Internal Target Volume contouring workflows process each phase in isolation, discarding temporal coherence and leaving …

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