Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

Ning, Wanshan; Lei, Shijun; Yang, Jingjing; Cao, Yukun; Jiang, Peiran; Yang, Qianqian; Zhang, Jiao; Wang, Xiaobei; Chen, Fenghua; Geng, Zhi; Xiong, Liang; Zhou, Hongmei; Guo, Yaping; Zeng, Yue; Shi, Heshui; Wang, Lin; Xue, Yu; Wang, Zheng

doi:10.1038/s41551-020-00633-5

Cited by 151 publications

(172 citation statements)

References 38 publications

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“…One of the challenges of fast and reliable COVID-19 diagnosis is the generalization of models to other datasets. We extend our findings to iCTCF-CT open-source dataset [NLY + 20], http://ictcf.biocuckoo.cn with Negative and COVID-19 classes, summarized in Table 6. We split the data randomly into 600 training/validation and 12976 test images (note the class imbalance in the test split) achieving the test/train+val ratio of 21.62.…”

Section: Resultssupporting

confidence: 55%

One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features

Ter-Sarkisov

2021

Preprint

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We introduce a model that segments lesions and predicts COVID-19 from chest CT scans through the derivation of an affinity matrix between lesion masks. The novelty of the methodology is based on the computation of the affinity between the lesion masks’ features extracted from the image. First, a batch of vectorized lesion masks is constructed. Then, the model learns the parameters of the affinity matrix that captures the relationship between features in each vector. Finally, the affinity is expressed as a single vector of pre-defined length. Without any complicated data manipulation, class balancing tricks, and using only a fraction of the training data, we achieve a 91.74% COVID-19 sensitivity, 85.35% common pneumonia sensitivity, 97.26% true negative rate and 91.94% F1-score. Ablation studies show that the method can quickly generalize to new datasets. All source code, models and results are publicly available on https://github.com/AlexTS1980/COVID-Affinity-Model.

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Section: Resultssupporting

confidence: 55%

One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features

Ter-Sarkisov

2021

Preprint

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“…iCTCF [34] dataset with 2 classes (Negative and COVID-19), from which we use 600 images (300/class) for training and validation and the remaining 12976 (COVID-19: 9275, Negative: 3701) for testing.…”

Section: Resultsmentioning

confidence: 99%

One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism

Ter-Sarkisov

2021

Preprint

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We present a model that fuses instance segmentation, Long Short-Term Memory Network and Attention mechanism to predict COVID-19 and segment chest CT scans. The model works by extracting a sequence of Regions of Interest that contain class-relevant information, and applies two Long Short-Term Memory networks with attention to this sequence to extract class-relevant features. The model is trained in one shot: both segmentation and classification branches, using two different sets of data. We achieve a 95.74% COVID-19 sensitivity, 98.13% Common Pneumonia sensitivity, 99.27% Control sensitivity and 98.15% class-adjusted F1 score on the main dataset of 21191 chest CT scan slices, and also run a number of ablation studies in which we achieve 97.73% COVID-19 sensitivity and 98.41% F1 score. All source code and models are available on https://github.com/AlexTS1980/COVID-LSTM-Attention.

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“…Although some teams developed similar deep learning-based tools for the diagnosis and risk stratification of COVID-19, none was compared with the conventional radiologist-based estimation involving the whole course of this disease [35][36][37] . In this study, all the data of 465 serial chest CT scans were involved in the correlation analysis between conventional CT scoring and novel deep learning-based quantification.…”

Section: Discussionmentioning

confidence: 99%

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

Pan

Liu

et al. 2021

Sci Rep

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This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

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Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

Cited by 151 publications

References 38 publications

One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features

One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features

One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

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