基于深度学习的X线胸片肺部描述自动生成

doi:10.16451/j.cnki.issn1003-6059.202106007

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Abstract The chest X-ray report automatic generation is a hot research topic in computer-aided diagnosis. More than 65% of diseases in chest X-rays are related to the lungs. For the generation of Chinese reports on lung descriptions, a hierarchical long short term memory model based on semantic labels is proposed. Firstly, the abnormal chest X-ray reports are analyzed, and high-frequency keywords are extracted as semantic labels. Then, the abnormal binary-classification module is introduced to correct the semantic label classification results. Finally, semantic labels and image features are fused to enhance the association mapping between them. Experimental results show that the proposed model is superior to the baseline method in both general and domain metrics, and it improves the performance of chest radiograph report generation effectively.

Key words： Chest X-Ray Semantic Label Hierarchical Long Short-Term Memory Chinese Report Lung Description

Received: 08 March 2021

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61962026), Youth Key Project of Natural Science Foundation of Jiangxi Province(No.20192ACBL21031), Science and Techno-logy Research Project of Jiangxi Provincial Department of Education(No.GJJ200318)

Corresponding Authors: CAO Yuanlong, Ph.D., associate professor. His research interests include machine learning and network security.

About author:: HUANG Xin, Ph.D., lecturer. His research interests include machine learning, bioinformatics and multi-modal data fusion.
GU Mengdan, master student. Her research interests include machine learning and medical information.
YI Yugen, Ph.D., associate professor. His research interests include artificial intelligence, computer vision and machine lear-ning.

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	HUANG Xin
	GU Mengdan
	YI Yugen
	CAO Yuanlong

Cite this article:

HUANG Xin,GU Mengdan,YI Yugen等. Automatic Generation of Lung Description in Chest X-Ray Based on Deep Learning[J]. , 2021, 34(6): 552-560.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202106007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I6/552

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