GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation
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 gaze guidance, allowing segmentation to extend beyond directly fixated regions while limiting se...
| Authors | Mohammed Oussama Benyahia, Marouane Tliba, Mohamed Amine Kerkouri et al. |
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| Primary category | eess.IV |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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