Active learning framework halves annotation effort for surgical videos

Researchers have developed a novel framework that combines active learning with dual-loss optimization to improve the efficiency of annotating surgical videos. This human-in-the-loop system uses a foundation model to generate class activation maps and iteratively refines pseudo-masks, guiding expert annotators to correct annotations. The proposed method significantly reduces annotation effort by up to 50% compared to traditional manual annotation, enabling the scalable development of surgical tool segmentation models. AI

IMPACT This research offers a practical method to reduce the cost and time associated with annotating surgical videos, potentially accelerating the development and deployment of AI tools for surgical analysis.

RANK_REASON The cluster contains a research paper detailing a new methodology for efficient annotation of surgical videos. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hugging Face
• Manasa Dendukuri

• paper
• product

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