Cross-Modal Knowledge Diffusion-Based Generation for Difference-Aware Medical VQA.

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Computer Vision

Background:

• Multimodal medical applications offer comprehensive support for healthcare.
• Difference-aware medical Visual Question Answering (VQA) analyzes changes in patient conditions over time.
• Existing methods face challenges due to data complexity, diversity, noise, and the need for multimodal medical knowledge.

Purpose of the Study:

• To propose a novel approach, MENDER (cross-Modal knowlEdge diffusioN-baseD gEneration netwoRk), for difference-aware medical VQA.
• To address the complexities of multimodal medical data and the image comparison requirements of difference-aware VQA.
• To enhance the understanding and generation of answers for medical VQA tasks involving temporal changes.

Main Methods:

• Utilized a diffusion mechanism with multi-step denoising for answer generation.
• Implemented answer nosing and knowledge cascading strategies tailored for medical VQA.
• Employed visual and structural knowledge injection using pre-trained medical image-text networks and heterogeneous graph Transformers to guide the diffusion process.

Main Results:

• Demonstrated the effectiveness of MENDER for difference-aware medical VQA tasks.
• Showcased MENDER's notable performance in low-resource settings.
• Validated MENDER's capability in conventional medical VQA tasks.

Conclusions:

• MENDER provides a robust solution for difference-aware medical VQA by effectively handling complex and noisy multimodal data.
• The proposed diffusion mechanism and knowledge injection strategies enhance the model's ability to recognize and report changes in physical conditions.
• MENDER shows promise for improving medical assistance systems, particularly in scenarios with limited data or standard VQA requirements.