Automatic Generation of Lung Description in Chest X-Ray Based on Deep Learning

被引：0
|
作者：
Huang X. [1 ,2 ]
Gu M. [1 ,2 ]
Yi Y. [1 ]
Cao Y. [1 ]
机构：
[1] School of Software, Jiangxi Normal University, Nanchang
[2] College of Electronic and Information Engineering, Tongji University, Shanghai
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Chest X-Ray; Chinese Report; Hierarchical Long Short-Term Memory; Lung Description; Semantic Label;
D O I：
10.16451/j.cnki.issn1003-6059.202106007
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
引用
收藏
页码：552 / 560
页数：8
相关论文
共 28 条
  • [1] RAJPURKA P, IRVIN J, ZHU K, Et al., ChexNet: Radiologist-Le-vel Pneumonia Detection on Chest X-Rays with Deep Learning[C/OL]
  • [2] YAO L, POBLENZ E, DAGUNTS D, Et al., Learning to Diagnose from Scratch by Exploiting Dependencies among Labels
  • [3] HUANG X, FANG Y, GU M D., Classification of Chest X-Ray Disease Based on Convolutional Neural Network, Journal of System Simulation, 32, 6, pp. 1188-1194, (2020)
  • [4] DEMNER-FUSHMAN D, KOHLI M D, ROSENMAN M B, Et al., Preparing a Collection of Radiology Examinations for Distribution and Retrieval, Journal of the American Medical Informatics Association, 23, 2, pp. 304-310, (2016)
  • [5] VINYALS O, TOSHEV A, BENGIO S, Et al., Show and Tell: A Neural Image Caption Generator, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156-3164, (2015)
  • [6] HE X W, YANG Y, SHI B G, Et al., VD-SAN: Visual-Densely Semantic Attention Network for Image Caption Generation, Neurocomputing, 328, pp. 48-55, (2019)
  • [7] YAO T, PAN Y W, LI Y H, Et al., Boosting Image Captioning with Attributes, Proc of the IEEE International Conference on Computer Vision, pp. 4904-4912, (2017)
  • [8] ZHANG J S, HONG Y, LI Z F, Et al., Image Captioning Based on Bidirectional Attention Mechanism, Journal of Chinese Information Processing, 34, 9, pp. 53-61, (2020)
  • [9] LI Z X, WEI H Y, HUANG F C, Et al., Combine Visual Features and Scene Semantics for Image Captioning, Chinese Journal of Computers, 43, 9, pp. 1624-1640, (2020)
  • [10] BI J Q, LIU M F, HU H J, Et al., Image Captioning Based on Dependency Syntax, Journal of Beijing University of Aeronautics and Astronautics, 47, 3, pp. 431-440, (2021)
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