# A Question-Centric Model for Visual Question Answering in Medical Imaging

Source: [A Question-Centric Model for Visual Question Answering in Medical Imaging](<https://www.sota2.com/research/paper/68fa67361e209131cea14dc6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:42.125Z

Authors: Minh H. Vu, Tommy L\\"ofstedt, Tufve Nyholm, Raphael Sznitman

Published: 2020-03-02T10:16:16.000Z

## Abstract

Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect strategies that estimate prediction uncertainties and visualize model support in the input image space, the ability to explicitly query a prediction model regarding its image content offers a more direct way to determine the behavior of trained models. To this end, we present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Medical Visual Question Answering on RIS-VQA (test)](<https://www.sota2.com/research/sota/medical-visual-question-answering-on-ris-vqa-test.md>): Medical Visual Question Answering; RIS-VQA (test); AUC: 67.7.

- [Medical Visual Question Answering on INSEGCAT-VQA (test)](<https://www.sota2.com/research/sota/medical-visual-question-answering-on-insegcat-vqa-test.md>): Medical Visual Question Answering; INSEGCAT-VQA (test); AUC: 0.801.

- [Visual Question Answering on DME-VQA](<https://www.sota2.com/research/sota/visual-question-answering-on-dme-vqa.md>): Visual Question Answering; DME-VQA; Overall Accuracy: 60.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.08760>)

[PDF](<https://arxiv.org/pdf/2003.08760.pdf>)