Localized Questions in Medical Visual Question Answering

Tascon-Morales, Sergio; Márquez-Neila, Pablo; Sznitman, Raphael (2023). Localized Questions in Medical Visual Question Answering. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Lecture Notes in Computer Science: Vol. 14221 (pp. 361-370). Springer 10.1007/978-3-031-43895-0_34

Preview

Text paper1317.pdf - Submitted Version Available under License Creative Commons: Attribution (CC-BY). Download (9MB) | Preview

Visual Question Answering (VQA) models aim to answer natural language questions about given images. Due to its ability to ask questions that differ from those used when training the model, medical VQA has received substantial attention in recent years. However, existing medical VQA models typically focus on answering questions that refer to an entire image rather than where the relevant content may be located in the image. Consequently, VQA models are limited in their interpretability power and the possibility to probe the model about specific image regions. This paper proposes a novel approach for medical VQA that addresses this limitation by developing a model that can answer questions about image regions while considering the context necessary to answer the questions. Our experimental results demonstrate the effectiveness of our proposed model, outperforming existing methods on three datasets. Our code and data are available at https://github.com/sergiotasconmorales/locvqa.

Interest & Impact

Downloads

0 since deposited on 05 Jul 2023
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item