A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis

Gozes, Ophir; Frid-Adar, Maayan; Sagie, Nimrod; Kabakovitch, Asher; Amran, Dor; Amer, Rula; Greenspan, Hayit

doi:10.1007/978-3-030-62469-9_8

Cited by 47 publications

(64 citation statements)

References 8 publications

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“…Such studies are more relevant in the current pandemic for global actions concerning verifiable scientific research against COVID-19. On the other hand, some studies merge multiple data sets and mention the source of data but do not host it as a separate repository [102]. The highly relevant studies have made public both data and code [49, 42].…”

Section: Comparisonmentioning

confidence: 99%

COVID-19 Open Source Data Sets: A Comprehensive Survey

Shuja

Alanazi

Alasmary

et al. 2020

Preprint

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In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID- 19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

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Section: Comparisonmentioning

confidence: 99%

COVID-19 Open Source Data Sets: A Comprehensive Survey

Shuja

Alanazi

Alasmary

et al. 2020

Preprint

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“…In [17], a convolutional neural network (CNN) using ResNet50 as the backbone to detect COVID-19 and distinguish it from community acquired pneumonia and other nonpneumonic lung diseases were proposed by using 4,356 3D chest CT exams. In [18], the authors developed an automated CT image analysis method to distinguish COVID-19 patients from those who do not have the disease by using deep learning on a testing set of 157 international patients. The two datasets in [17,18] are not open due to some reasons of privacy protection.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], the authors developed an automated CT image analysis method to distinguish COVID-19 patients from those who do not have the disease by using deep learning on a testing set of 157 international patients. The two datasets in [17,18] are not open due to some reasons of privacy protection. Consequently, researchers tried to build public COVID-19 dataset in their researches [19, 20].…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

Ruan

Xiang

et al. 2020

Preprint

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Background Coronavirus disease 2019 has surprised the world since the beginning of 2020, and the rapid growth of COVID-19 is beyond the capability of doctors and hospitals that could deal in many areas. The chest computed tomography (CT) could be served as an effective tool in detection of COVID-19. It is valuable to develop automatic detection of COVID-19. Materials and MethodsThe collected dataset consisted of 1042 chest CT images (including 521 COVID-19, 397 healthy, 76 bacterial pneumonia and 48 SARS) obtained by exhaustively searching available data on the Internet. Then, these data are divided into three sets, referred to training set, validation set and testing set. Sixteen data augmentation operations are designed to enrich the training set in deep learning training phase. Multiple experiments were conducted to analyze the performance of the model in the detection of COVID-19 both in case of no noisy labels and noisy labels. The performance was assessed by the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Results The data augmentation operations on the training set are effective for improvement of the model performance. The area under the receiver operating characteristic curve is 0.9689 with (95% CI: 0.9308, 1) in case of no noisy labels for the classification of COVID-19 from heathy subject, while the per-exam sensitivity, specificity and accuracy for detecting COVID-19 in the independent testing set are 90.52%, 91.58% and 91.21%, respectively. In the classification of COVID-19 from other hybrid cases, the average AUC of the proposed model is 0.9222 with (95%CI: 0.8418, 1) if there are no noisy labels. The model is also robust when part of the training samples is marked incorrectly. The average AUC is 92.23% in the case of noisy labels of 10% in the training set. Conclusion A deep learning model with insufficient samples can be developed by using data augmentation in assisting medical workers in making quick and correct diagnosis of COVID-19.

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“…Machine learning and regression models have been used to classify the novel pathogen from its genetic sequencing [13], to support diagnosis from CT scans [14], to assist clinical prognosis of patients [15], and to forecast the evolution of the pandemic [16]. Regression models trained with patient-reported symptoms and laboratory test results have been used to predict infection from symptomatology [17].…”

Section: Introductionmentioning

confidence: 99%

Smart Pooling: AI-powered COVID-19 testing

Escobar¹,

Jeanneret²,

Bravo-Sánchez³

et al. 2020

Preprint

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Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance testing efficiency, but they are viable only at very low prevalences of the disease. We propose Smart Pooling, a machine learning method that uses sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. We show efficiency gains of 42% with respect to individual testing at disease prevalence of up to 25%, a regime in which two-step pooling offers marginal efficiency gains. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-step pooling strategies and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory.

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A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis

Cited by 47 publications

References 8 publications

COVID-19 Open Source Data Sets: A Comprehensive Survey

COVID-19 Open Source Data Sets: A Comprehensive Survey

Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

Smart Pooling: AI-powered COVID-19 testing

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