DualPrompt-MedCap: A Dual-Prompt Enhanced Approach for Medical Image Captioning

• URL: http://arxiv.org/abs/2504.09598v1
• Date: Sun, 13 Apr 2025 14:31:55 GMT
• Title: DualPrompt-MedCap: A Dual-Prompt Enhanced Approach for Medical Image Captioning
• Authors: Yining Zhao, Ali Braytee, Mukesh Prasad,
• Abstract summary: We present DualPrompt-MedCap, a novel dual-prompt enhancement framework that augments Large Vision-Language Models (LVLMs)<n>A modality-aware prompt derived from a semi-supervised classification model pretrained on medical question-answer pairs, and a question-guided prompt leveraging biomedical language model embeddings.<n>Our method enables the generation of clinically accurate reports that can serve as medical experts' prior knowledge and automatic annotations for downstream vision-language tasks.
• Score: 5.456249017636404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image captioning via vision-language models has shown promising potential for clinical diagnosis assistance. However, generating contextually relevant descriptions with accurate modality recognition remains challenging. We present DualPrompt-MedCap, a novel dual-prompt enhancement framework that augments Large Vision-Language Models (LVLMs) through two specialized components: (1) a modality-aware prompt derived from a semi-supervised classification model pretrained on medical question-answer pairs, and (2) a question-guided prompt leveraging biomedical language model embeddings. To address the lack of captioning ground truth, we also propose an evaluation framework that jointly considers spatial-semantic relevance and medical narrative quality. Experiments on multiple medical datasets demonstrate that DualPrompt-MedCap outperforms the baseline BLIP-3 by achieving a 22% improvement in modality recognition accuracy while generating more comprehensive and question-aligned descriptions. Our method enables the generation of clinically accurate reports that can serve as medical experts' prior knowledge and automatic annotations for downstream vision-language tasks.

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