GAZE framework with viewer tools and literature retrieval achieves 58.2 mAP@0.3 lesion localization and 34.9% top-1 diagnostic accuracy on 906 rare brain MRI cases in zero-shot setting, with larger gains on rarest pathologies.
arXiv preprint arXiv:2504.20930 , year=
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GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI
GAZE framework with viewer tools and literature retrieval achieves 58.2 mAP@0.3 lesion localization and 34.9% top-1 diagnostic accuracy on 906 rare brain MRI cases in zero-shot setting, with larger gains on rarest pathologies.
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Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis
TIF-GRPO uses integral feedback on pseudo-temporal trajectories to regulate anatomy-aware rewards in RL for clinical faithfulness in volumetric CT analysis.
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A Survey of Reinforcement Learning for Large Reasoning Models
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