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A Dual-Attention Learning Network With Word and Sentence Embedding for Medical Visual Question Answering.

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成果类型:
期刊论文
作者:
Xiaofei Huang;Hongfang Gong
作者机构:
[Xiaofei Huang; Hongfang Gong] School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, China
语种:
英文
期刊:
IEEE Transactions on Medical Imaging
ISSN:
0278-0062
年:
2024
卷:
43
期:
2
页码:
832-845
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61972055) 10.13039/501100004735-Natural Science Foundation of Hunan Province (Grant Number: 2021JJ30734)
机构署名:
本校为第一机构
院系归属:
数学与统计学院
摘要:
Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such ...

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