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Published in:
2020 | OriginalPaper | Chapter
Learning from the Guidance: Knowledge Embedded Meta-learning for Medical Visual Question Answering
Authors : Wenbo Zheng, Lan Yan, Fei-Yue Wang, Chao Gou
Published in: Neural Information Processing
Publisher: Springer International Publishing
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Abstract
Traditional medical visual question answering approaches require a large amount of labeled data for training, but still cannot jointly consider both image and text information. To address this issue, we propose a novel framework called Knowledge Embedded Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. First, we introduce the embedding approach to perform feature fusion representation learning. Then, we present the construction of our knowledge graph that relates image with text, as the guidance of our meta-learner. We design a knowledge embedding mechanism to incorporate the knowledge representation into our network. Final result is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with other state-of-the-arts.