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[Image-aware generative medical visual question answering based on image caption prompts].

Rui Wang1, Jiana Meng1, Yuhai Yu1

  • 1Computer Science and Engineering College, Dalian Minzu University, Dalian, Liaoning 116650, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|June 26, 2025
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Summary

This study introduces an image-aware generative method for medical visual question answering (MVQA) that improves accuracy in low-resource settings. The novel approach uses image caption prompts for better understanding, outperforming existing models.

Keywords:
Computer-aided diagnosisImage captions promptImage-aware generative modelMedical visual question answering

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Computer-Aided Diagnosis

Context:

  • Medical visual question answering (MVQA) is vital for computer-aided diagnosis and telemedicine.
  • Existing MVQA methods struggle with limited, unevenly annotated datasets, leading to overfitting in low-resource domains.
  • Current approaches often rely on external datasets and predefined label sets, limiting flexibility.

Purpose:

  • To develop an image-aware generative MVQA method using image caption prompts.
  • To address limitations of existing methods in low-resource domains.
  • To enhance the understanding of medical image information for accurate answer generation.

Summary:

  • A dual visual feature extractor and progressive bilinear attention interaction module extract multi-level image features.
  • An image caption prompt method guides the model to interpret image data effectively.
  • An image-aware generative model generates answers, demonstrating superior performance and efficiency.

Impact:

  • The proposed method outperforms existing models on MVQA tasks, especially in low-resource domains.
  • It enables efficient visual feature extraction and flexible, accurate answer generation with low computational cost.
  • Significantly contributes to personalized precision medicine, reduced medical burden, and improved diagnostic efficiency.