Q-FSRU: Quantum-Augmented Frequency-Spectral For Medical Visual Question Answering
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 28 Sep 2025.
Builds on
16 of 21 references.
References · 16
- 2017Attention Is All You Needcited by 60,361
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2015VQA: Visual Question Answeringcited by 2,506
- 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,553
- 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
- 2019Deep Modular Co-Attention Networks for Visual Question Answeringcited by 339
- 2021Medical Visual Question Answering: A Surveycited by 93
- 2024Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicinecited by 28
- 2024STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answeringcited by 21
- 2024MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensorscited by 17
- 2024LaPA: Latent Prompt Assist Model For Medical Visual Question Answeringcited by 12
- 2020A Survey of Quantum Theory Inspired Approaches to Information Retrievalcited by 9
4 more
- 2023Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detectorcited by 9
- 2024Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understandingcited by 7
- 2025Quantum-inspired Embeddings Projection and Similarity Metrics for Representation Learningcited by 4
- 2025FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentationcited by 3
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Q-FSRU: Quantum-Augmented Frequency-Spectral Fusion for Medical Visual Question Answering
- 2025Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA
- 2026QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG
- 2023RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training
- 2025BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering
- 2025Adapting Lightweight Vision Language Models for Radiological Visual Question Answering
- 2024Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis
- 2026Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering