Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke

• URL: http://arxiv.org/abs/2605.14710v1
• Date: Thu, 14 May 2026 11:28:57 GMT
• Title: Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke
• Abstract summary: This paper proposes a novel tri-modal fusion model for ischemic stroke prognosis.<n>We employ a Large Language Model (LLM) to automatically generate semi-structured diagnostic text from brain MRIs.<n>We also design a core component termed the Vision-Conditioned Dual Alignment Fusion Module.
• Score: 4.272151451487239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning and multi-modal fusion have demonstrated transformative potential in medical diagnosis by integrating diverse data sources. However, accurate prognosis for ischemic stroke remains challenging due to limitations in existing multi-modal approaches. First, current methods are predominantly confined to dual-modal fusion, lacking a framework that effectively integrates the trifecta of medical images, structured clinical data, and unstructured text. Second, they often fail to establish deep bidirectional interactions between modalities; To address these critical gaps, this paper proposes a novel tri-modal fusion model for ischemic stroke prognosis. Our approach first enriches the data representation by employing a Large Language Model (LLM) to automatically generate semi-structured diagnostic text from brain MRIs. This process not only addresses the scarcity of expert annotations but also serves as a regularized semantic enhancement, improving multimodal fusion robustness. Furthermore, we design a core component termed the Vision-Conditioned Dual Alignment Fusion Module (VDAFM), which strategically uses visual features as a conditional prior to guide fine-grained interaction with the generated text. This module achieves a dynamic and profound fusion through a dual semantic alignment loss, effectively mitigating modal heterogeneity. Extensive experiments on a real-world clinical dataset demonstrate that our model achieves state-of-the-art performance.

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