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GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Medical image segmentation remains difficult to scale because high-performing methods typically rely on dense expert annotations and task-specific training. We introduce GazeRefine, a training-free framework that uses gaze as an inference-time prompt for zero-shot medical image segmentation. Sparse, duration-weighted fixations are converted into foreground and background priors that initialize semantic prototypes in frozen DINOv3 feature space. These prototypes are iteratively refined through foreground-background discrimination, feature-space affinity propagation, and anchoring to the initial

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