DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation

Cross-modal alignment of visual and textual representations is fundamental to multimodal medical image understanding, yet remains hindered by uncertainty in both modalities under real-world clinical conditions. Existing vision-language segmentation methods rely on deterministic cross-modal matching, which overlooks aleatoric uncertainty from ambiguous boundaries and epistemic uncertainty from limited training data, leading to fragile performance under domain shift. To address this issue, we prop

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ethics.ai (6 August 2026), “DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation,” evidence record 17087, https://ethics.ai/record/17087 (originally published by arXiv).

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