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Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Pretrained vision-language models (VLMs) have shown promising performance in medical image segmentation by incorporating clinical text. However, it remains unclear how much textual information actually contributes to pixel-level predictions. In this work, we systematically investigate the role of text in multimodal medical image segmentation. We first analyze several commonly used fusion strategies and find that segmentation performance is largely insensitive to the choice of fusion module. To further understand modality interactions, we propose an Evidence Decoupling Decoder (EDD) based on ev

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