Multiple Meta-model Quantifying for Medical Visual Question Answering

Do, Tuong ORCID: 0000-0002-3290-3787, Nguyen, Binh X, Tjiputra, Erman, Tran, Minh, Tran, Quang D and Nguyen, Anh ORCID: 0000-0002-1449-211X (2021) Multiple Meta-model Quantifying for Medical Visual Question Answering. .

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Abstract

Transfer learning is an important step to extract meaningful features and overcome the data limitation in the medical Visual Question Answering (VQA) task. However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized. In this paper, we present a new multiple meta-model quantifying method that effectively learns meta-annotation and leverages meaningful features to the medical VQA task. Our proposed method is designed to increase meta-data by auto-annotation, deal with noisy labels, and output meta-models which provide robust features for medical VQA tasks. Extensively experimental results on two public medical VQA datasets show that our approach achieves superior accuracy in comparison with other state-of-the-art methods, while does not require external data to train meta-models. Source code available at: https://github.com/aioz-ai/MICCAI21_MMQ.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Eye Disease and Disorders of Vision, Generic health relevance
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	08 Apr 2024 10:25
Last Modified:	08 Apr 2024 10:25
DOI:	10.1007/978-3-030-87240-3_7
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3180138