Uncertainty-Aware Vision-Language Segmentation for Medical Imaging

• URL: http://arxiv.org/abs/2602.14498v2
• Date: Fri, 20 Feb 2026 13:24:13 GMT
• Title: Uncertainty-Aware Vision-Language Segmentation for Medical Imaging
• Authors: Aryan Das, Tanishq Rachamalla, Koushik Biswas, Swalpa Kumar Roy, Vinay Kumar Verma,
• Abstract summary: We introduce a novel uncertainty-aware multimodal segmentation framework for medical diagnosis.<n>We propose a Modality Decoding Attention Block (MoDAB) with a lightweight State Space Mixer (SSMix) to enable efficient cross-modal fusion.<n>Our results highlight the importance of incorporating uncertainty modelling and structured modality alignment in vision-language medical segmentation tasks.
• Score: 12.545486211087791
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel uncertainty-aware multimodal segmentation framework that leverages both radiological images and associated clinical text for precise medical diagnosis. We propose a Modality Decoding Attention Block (MoDAB) with a lightweight State Space Mixer (SSMix) to enable efficient cross-modal fusion and long-range dependency modelling. To guide learning under ambiguity, we propose the Spectral-Entropic Uncertainty (SEU) Loss, which jointly captures spatial overlap, spectral consistency, and predictive uncertainty in a unified objective. In complex clinical circumstances with poor image quality, this formulation improves model reliability. Extensive experiments on various publicly available medical datasets, QATA-COVID19, MosMed++, and Kvasir-SEG, demonstrate that our method achieves superior segmentation performance while being significantly more computationally efficient than existing State-of-the-Art (SoTA) approaches. Our results highlight the importance of incorporating uncertainty modelling and structured modality alignment in vision-language medical segmentation tasks. Code: https://github.com/arya-domain/UA-VLS

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