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ARTEMIS framework improves video polyp segmentation with agent-guided temporal mask evolution

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ARTEMIS, a novel framework for video polyp segmentation that utilizes agent-guided temporal mask evolution to improve accuracy with imperfect supervision. The system leverages tools like SAM2 to generate initial masks from sparse inputs such as points or scribbles, and employs a vision-language agent to identify reliable temporal anchors. ARTEMIS then refines these masks by propagating reliable information bidirectionally and trains the segmentation model using a robust learning approach that accounts for mask reliability and down-weights noisy supervision. AI

IMPACT This research advances techniques for medical image analysis, potentially improving diagnostic accuracy in video-based procedures.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for video polyp segmentation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ARTEMIS framework improves video polyp segmentation with agent-guided temporal mask evolution

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The cluster contains a research paper published on arXiv detailing a new method for video polyp segmentation.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Tong Wang, Siwen Wang, Yaolei Qi, Jinxing Zhou, Yuting He, Guanyu Yang, Yutong Xie ·

    ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation

    arXiv:2606.20161v1 Announce Type: new Abstract: Imperfectly supervised video polyp segmentation (VPS) aims to learn dense, temporally consistent masks from inexpensive supervision, including weak annotations (points, scribbles) and semi-supervision with few densely labeled frames…

  2. arXiv cs.CV TIER_1 English(EN) · Yutong Xie ·

    ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation

    Imperfectly supervised video polyp segmentation (VPS) aims to learn dense, temporally consistent masks from inexpensive supervision, including weak annotations (points, scribbles) and semi-supervision with few densely labeled frames. This setting is clinically valuable but challe…

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