TIF-GRPO uses integral feedback on pseudo-temporal trajectories to regulate anatomy-aware rewards in RL for clinical faithfulness in volumetric CT analysis.
Nature Communications , volume=
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Reweighting the training loss to emphasize semantically salient tokens lets ophthalmological report generation models reach similar quality with up to ten times less data.
citing papers explorer
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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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Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Reweighting the training loss to emphasize semantically salient tokens lets ophthalmological report generation models reach similar quality with up to ten times less data.