Hypergraph learning for identification of COVID-19 with CT imaging

Di, Donglin; Shi, Feng; Yan, Fuhua; Xia, Liming; Mo, Zhanhao; Ding, Zhongxiang; Shan, Fei; Song, Bin; Li, Shengrui; Wei, Ying; Shao, Ying; Han, Meisheng; Gao, Yaozong; Sui, He; Gao, Yue; Shen, Dinggang

doi:10.1016/j.media.2020.101910

Cited by 72 publications

(48 citation statements)

References 29 publications

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“…In outbreak areas, COVID-19 patients are in urgent need of diagnosis. Due to fast acquisition, some works perform X-ray ( Wong, Lam, Fong, Leung, Chin, Lo, Lui, Lee, Chiu, Chung, Lee, Wan, Hung, Lam, Kuo, Ng, 2019 , Sitaula, Hossain, 2020 , Minaee, Kafieh, Sonka, Yazdani, Soufi, 2020 ) and CT scans ( Di, Shi, Yan, Xia, Mo, Ding, Shan, Li, Wei, Shao, Han, Gao, Sui, Gao, Shen, 2020 , Yang, Xu, Li, Myronenko, Roth, Harmon, Xu, Turkbey, Turkbey, Wang, Zhu, Carrafiello, Patella, Cariati, Obinata, Mori, Tamura, An, Wood, Xu, 2021 , Gao, Su, Jiang, Zeng, Feng, Shen, Rong, Xu, Qin, Yang, Wang, Hu, 2020 ) to identify COVID-19. Besides early screening, the study of malignant progression prediction is also important for treatment planning.…”

Section: Related Workmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2021

Medical Image Analysis

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Section: Related Workmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2021

Medical Image Analysis

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“…(2020) proposed an online attention module in 3D CNN to identify COVID-19 from CAP with a dual sample strategy to tackle the imbalanced distributions of the size of infection regions. Di et al. (2020) proposed a uncertainty vertex-weighted hypergraph learning method for identifying COVID-19 from CAP with image features and radiometric features.…”

Section: Related Workmentioning

confidence: 99%

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information

Xue

Cao

Liu

et al. 2021

Medical Image Analysis

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“…COVID-19 and community-acquired pneumonia (CAP) have very similar clinical manifestations and imaging features in CT images. To differentiate the confusing cases in these two groups, Di et al (2021) have designed an uncertainty vertex-weighted hypergraph learning (UVHL) method to identify COVID-19 from CAP. In this method, a hypergraph structure is constructed where each vertex corresponds to a sample and hyperedges connect neighbor vertices that share common features.…”

Section: Covid-19 Detection and Diagnosismentioning

confidence: 99%

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Deng

2021

Front. Artif. Intell.

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Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients’ check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

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Hypergraph learning for identification of COVID-19 with CT imaging

Cited by 72 publications

References 29 publications

Deep learning for predicting COVID-19 malignant progression

Deep learning for predicting COVID-19 malignant progression

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

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