DOMAS: Data Oriented Medical Visual Question Answering Using Swin Transformer

  • T.-A. Toader Department of Computer Science, Babes-Bolyai University, 1, M. Kogalniceanu Street, 400084, Cluj-Napoca, Romania


The Medical Visual Question Answering problem is a joined Computer Vision and Natural Language Processing task that aims to obtain answers in natural language to a question, posed in natural language as well, regarding an image. Both the image and question are of a medical nature. In this paper we introduce DOMAS, a deep learning model that solves this task on the Med-VQA 2019 dataset. The method is based on dividing the task into smaller classification problems by using a BERT-based question classification and a unique approach that makes use of dataset information for selecting the suited model. For the image classification problems, transfer learning using a pre-trained Swin Transform based architecture is used. DOMAS uses a question classifier and seven image classifiers along with the image classifier selection strategy and achieves 0.616 strict accuracy and 0.654 BLUE score. The results are competitive with other state-of-the-art models, proving that our approach is effective in solving the presented task.


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How to Cite
TOADER, T.-A.. DOMAS: Data Oriented Medical Visual Question Answering Using Swin Transformer. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 68, n. 1, p. 55-70, july 2023. ISSN 2065-9601. Available at: <>. Date accessed: 21 july 2024. doi: