Q-FSRU: Quantum-Augmented Frequency-Spectral Fusion for Medical Visual Question Answering
Rakesh Thakur, Yusra Tariq
TL;DR
Q-FSRU introduces a novel medical VQA model combining frequency domain analysis and quantum-inspired retrieval to enhance accuracy and explainability in healthcare AI applications.
Contribution
It integrates frequency spectrum features with quantum retrieval techniques for improved medical visual question answering performance.
Findings
Outperforms previous models on VQA-RAD dataset
Enhances reasoning in complex medical cases
Improves model explainability
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…
Peer Reviews
Decision·Submitted to ICLR 2026
Experimental results on the VQA-RAD and PathVQA datasets show that Q-FSRU outperforms previous models in accuracy, F1-score, and AUC.
1. The paper does not provide enough information about how to train its model and the comparison methods. 2. Also, many evaluation details are missing, particularly regarding how the open-ended questions are used for evaluation. 3. How the RAG works is not clear, and it is also unclear which database is used for retrieval. 4. VQA-RAD and PathVQA are datasets from very different domains. However, the proposed method does not use any large-scale medical dataset for pretraining. How can a model
The method achieves significant improvements on the VQA-RAD and PathVQA datasets and includes comprehensive experiments and ablation studies.
1. The evaluation dataset is relatively limited in scale, with the main evaluation conducted on VQA-RAD, a comparatively small dataset containing only 3,515 question–answer pairs. Although PathVQA (32,799 question–answer pairs) was also used, it served only for zero-shot generalization testing rather than as the primary training and evaluation benchmark. Therefore, the model’s remarkable performance on a small-scale dataset may not fully demonstrate its scalability and robustness on larger datas
- It combines frequency-domain processing with quantum-inspired knowledge retrieval, filling a gap in Med-VQA research. - It conducts comprehensive experiments, including in-domain evaluations on VQA-RAD and cross-dataset generalization tests on PathVQA, fully validating the model’s effectiveness and transferability.
- The problem to be solved and the limitations of existing methods are not clearly articulated; the paper fails to explicitly and systematically elaborate on the core pain points of current Med-VQA models and how these limitations affect clinical application scenarios. - The motivation for adopting quantum-inspired retrieval augmentation is unclear; the paper does not sufficiently explain why quantum-based similarity techniques are more suitable for medical knowledge retrieval than mature classi
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification