MedFuseNet: Attention-based Multimodal deep learning model for Visual Question Answering in the Medical Domain

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NSR21.pdf (6.66 MB)

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https://people.cs.vt.edu/~reddy/papers/NSR21.pdf

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http://hdl.handle.net/11603/22992

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UMBC Information Systems Department
UMBC Faculty Collection

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2021

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journal articles
preprints
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Sharma, Dhruv; Purushotham, Sanjay; Reddy, Chandan K.; MedFuseNet: Attention-based Multimodal deep learning model for Visual Question Answering in the Medical Domain; Virginia Polytechnic Institute and State University, 2021; https://people.cs.vt.edu/~reddy/papers/NSR21.pdf

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Abstract

Medical images are difficult to comprehend for a person without expertise. The limited number of practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human-errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision-maker. Thus, it becomes crucial to have a reliable Visual Question Answering (VQA) system to provide a ‘second opinion’ on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction - categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrates that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model’s predicted results.