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.
- alphaXiv
- ARTEMIS
- arXiv
- CatalyzeX
- CVC-ClinicDB-612
- DagsHub
- Gotit.pub
- Hugging Face
- SAM2
- ScienceCast
- SUN-SEG
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