arXiv (All)
open access
GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation
68t07artificial-intelligencecomputer-vision-and-pattern-recognitionhuman-computer-interactioni-2-10image-and-video-processingmachine-learning
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,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation#627707
- OmniMedSeg: A Large-Scale Standardized Benchmark for Interactive Medical Image Segmentation#276099
- Test-time adaptation with salip: A cascade of sam and clip for zero-shot medical image segmentation#442284
- GR-FM: Geometrically Regularized Flow Matching for SDF-Based Medical Image Segmentation#1129610
- Vascular-aware mixture-of-experts with a texture-enhanced decoder for accurate X-ray vessel segmentation.#1167237
Record · ID 821259
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive