This research introduces an efficient bilinear attention fusion mechanism for medical visual question answering (MedVQA) systems that help clinicians interpret medical images.

• Proposes a lightweight alternative to large pretrained visual-language models
• Achieves improved performance through specialized attention mechanisms
• Enables more efficient processing of medical image-question pairs
• Supports clinical decision-making in radiology workflows

By advancing MedVQA technology, this research helps reduce radiologist workload while maintaining diagnostic accuracy. The approach prioritizes computational efficiency without sacrificing performance—critical for real-world clinical applications.

Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering