Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning

Han, Zhongyi; Wei, Benzheng; Hong, Yanfei; Li, Tianyang; Cong, Jinyu; Zhu, Xue Li; Wei, Haifeng; Zhang, Wei

doi:10.1109/tmi.2020.2996256

Cited by 277 publications

(202 citation statements)

References 31 publications

Supporting

Mentioning

185

Contrasting

Unclassified

Order By: Relevance

“…Han et al [ 44 ] proposed a patient-level attention-based deep 3D multiple instance learning (AD3D-MIL) that learns Bernoulli distributions of the labels obtained by a pooling approach. They used a total of 460 chest CT examples, 230 CT examples from 79 COVID-19 confirmed patients, 100 CT examples from 100 patients with pneumonia, and 130 CT examples from 130 people without pneumonia.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Ozsahin¹,

Şekeroğlu²,

Musa³

et al. 2020

Computational and Mathematical Methods in Medicine

156

102

View full text Add to dashboard Cite

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In addition to COVID-19/non-COVID-19 classification, Han et al [ 44 ] performed experiments to classify COVID-19, common pneumonia, and no pneumonia cases as three classes classification. Their proposed AD3D-MIL model achieved an accuracy, AUC, and the Cohen kappa score of 94.3%, 98.8%, and 91.1%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Ozsahin¹,

Şekeroğlu²,

Musa³

et al. 2020

Computational and Mathematical Methods in Medicine

156

102

View full text Add to dashboard Cite

show abstract

“…Several slice-level diagnosis methods 17,26,27 were proposed which were quite similar to Li et al's work. Some AI systems employed 3D convolution neural networks, but only considered the relatively simple two-category classification 28,29 . There are also a few COVID-19 detection systems using CXR 30 , but the number of subjects with COVID-19 in these studies is much smaller than that in the studies using CT, and no study has quantitively compared performances of CXR and CT using paired data.…”

mentioning

confidence: 99%

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

et al. 2020

View full text Add to dashboard Cite

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

show abstract

“…Recently, several works focused on accurate diagnosis under weak supervision have been proposed. Notably, we consider approaches that use (a) patch-based Jin et al (2020); Shi et al (2020b) (b) slice-based Gozes et al (2020b,a); Hu et al (2020), and (c) 3D CT-based Han et al (2020); Zheng et al (2020) methods for diagnostic decisions. The first often uses prior segmented infection regions as input to train classifiers in a two-stage setup.…”

Section: Introductionmentioning

confidence: 99%

Dual Attention Multiple Instance Learning with Unsupervised Complementary Loss for COVID-19 Screening

Chikontwe

Luna

Kang

et al. 2020

Preprint

View full text Add to dashboard Cite

Chest computed tomography (CT) based analysis and diagnosis of the Coronavirus Disease 2019 (COVID-19) plays a key role in combating the outbreak of the pandemic that has rapidly spread worldwide. To date, the disease has infected more than 18 million people with over 690k deaths reported. Reverse transcription polymerase chain reaction (RT-PCR) is the current gold standard for clinical diagnosis but may produce false positives; thus, chest CT based diagnosis is considered more viable. However, accurate screening is challenging due to difficulty in annotation efforts of infected areas, curation of large datasets, and the slight discrepancies between COVID-19 and other viral pneumonia. In this study, we propose an attention-based end-to-end weakly supervised framework for the rapid diagnosis of COVID-19 and bacterial pneumonia based on multiple instance learning (MIL). We further incorporate unsupervised contrastive learning for improved accuracy with attention applied both in spatial and latent contexts, herein we propose Dual Attention Contrastive based MIL (DA-CMIL). DA-CMIL takes as input a several patient CT slices (considered as a bag of instances) and outputs a single label. Attention based pooling is applied to implicitly select key slices in latent space, and spatial attention learns slice spatial context for interpretable diagnosis. A contrastive loss is applied at the instance level to encode similarity in features from the same patient against pooled patient features. Empirical results show our algorithm achieves an overall accuracy of 98.6% and an AUC of 98.4%. Moreover, ablation studies show the benefit of contrastive learning with MIL.

show abstract

Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning

Cited by 277 publications

References 31 publications

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Dual Attention Multiple Instance Learning with Unsupervised Complementary Loss for COVID-19 Screening

Contact Info

Product

Resources

About