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.
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Acknowledgment
This work is supported in part by the Key Research and Development Program of Guangzhou (202007050002), in part by the National Natural Science Foundation of China (61806198, 61533019, U1811463), and in part by the National Key Research and Development Program of China (No. 2018AAA0101502).
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Zheng, W., Yan, L., Wang, FY., Gou, C. (2020). Learning from the Guidance: Knowledge Embedded Meta-learning for Medical Visual Question Answering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_22
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DOI: https://doi.org/10.1007/978-3-030-63820-7_22
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