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Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

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Author(s)
Aoxiao Zhongiang LiaDufan WuHui RenKyungsang KimYounggon KimVarun BuchNir NeumarkBernardo BizzoWon Young TakSoo Young ParkYu Rim LeeMin Kyu KangJung Gil ParkByung Seok KimWoo Jin ChungNing GuoIttai DayanMannudeep K. KalraQuanzheng Li
Keimyung Author(s)
Chung, Woo Jin
Department
Dept. of Internal Medicine (내과학)
Journal Title
Med Image Anal
Issued Date
2021
Volume
70
Keyword
Chest radiographCOVID-19Image retrievalImage content query
Abstract
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Keimyung Author(s)(Kor)
정우진
Publisher
School of Medicine (의과대학)
Citation
Aoxiao Zhong et al. (2021). Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19. Med Image Anal, 70, 101993. doi: 10.1016/j.media.2021.101993
Type
Article
ISSN
1361-8423
Source
https://www.sciencedirect.com/science/article/abs/pii/S1361841521000396
DOI
10.1016/j.media.2021.101993
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43637
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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