Title: Q-FSRU: Quantum-Augmented Frequency-Spectral Fusion for Medical Visual Question Answering
Primary area: generative models
Keywords: Medical VQA, Frequency Spectrum Representation, Fast Fourier Transform (FFT), Quantum Retrieval-Augmented Generation, Image-text reasoning, Radiology, Explainable AI, Clinical decision support

Abstract: Solving tough clinical questions that require both image and text understanding is still a major challenge in healthcare AI. In this work, we propose Q-FSRU, a new model that combines Frequency Spectrum Representation and Fusion (FSRU) with a method called Quantum Retrieval-Augmented Generation (Quantum RAG) for medical Visual Question Answering (VQA). The model takes in features from medical images and related text, then shifts them into the frequency domain using Fast Fourier Transform (FFT). This helps it focus on more meaningful data and filter out noise or less useful information. To improve accuracy and ensure that answers are based on real knowledge, we add a quantum-inspired retrieval system. It fetches useful medical facts from external sources using quantum-based similarity techniques. These details are then merged with the frequency-based features for stronger reasoning. We evaluated our model using the VQA-RAD dataset, which includes real radiology images and questions. The results showed that Q-FSRU outperforms earlier models, especially on complex cases needing image-text reasoning. The mix of frequency and quantum information improves both performance and explainability. Overall, this approach offers a promising way to build smart, clear, and helpful AI tools for doctors.

TL;DR: We propose Q-FSRU, a medical VQA model that fuses frequency-domain features with quantum-inspired retrieval, achieving superior accuracy and explainability on complex radiology questions.