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Severity and Consolidation Quantification of COVID-19 from CT Images Using Deep Learning Based on Hybrid Weak Labels
2020
IEEE journal of biomedical and health informatics
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In this work, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information ...
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. ...
In this work, we proposed a deep learning approach to learn the infection and consolidation information from CT images based on hybrid weak labels: patient-level multi-class information and manually labelled ...
doi:10.1109/jbhi.2020.3030224
pmid:33044938
fatcat:unn7pyzyq5bhngeobrzi62ehr4
Table of Contents
2020
IEEE journal of biomedical and health informatics
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Tam 3520 SPECIAL ISSUE ON AI-DRIVEN INFORMATICS Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels . . . . . . . . . . . . . . . . . . ...
Tian 3576 Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/jbhi.2020.3039663
fatcat:bdrgkdmzozd47iyy53ww2u3ngi
COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans
[article]
2021
arXiv
pre-print
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Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) scans can help determining the disease stage, efficiently allocating limited healthcare resources, and ...
Performance of the proposed COVID-Rate framework is evaluated through several experiments based on the introduced and external datasets. ...
Data/Code Availability The datasets/codes generated and/or analyzed during the current study will be released publicly upon potential publication of the study. ...
arXiv:2107.01527v1
fatcat:tnoki62cqbfxvoi2kvf7tom5m4
The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review
2021
Diagnostics
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As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT. ...
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/diagnostics11081317
fatcat:prsewme7s5hojmb4rzk2sfgwa4
A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises
[article]
2020
arXiv
pre-print
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We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. ...
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called ...
COVID-19 patients for which the first decision support applications based on deep learning have already appeared [91] . ...
arXiv:2008.09104v1
fatcat:z2gic7or4vgnnfcf4joimjha7i
COVID-view: Diagnosis of COVID-19 using Chest CT
[article]
2021
arXiv
pre-print
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Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. ...
We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. ...
Here, we build a COVID-19 classification model based on deep multiple instance learning (MIL) to address the problem of weakly annotated data in chest CT. ...
arXiv:2108.03799v1
fatcat:egk5kpzeprdrfbj42c47ylbjde
Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19
2021
EAI Endorsed Transactions on Pervasive Health and Technology
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The study in [19] has introduced a new attention-based deep 3D multiple instance learning (AD3D-MIL) for the screening of COVID-19 with weak labels. ...
In [27] , CNN based transfer learning method was used for detecting COVID-19 from X-ray images. ...
doi:10.4108/eai.28-1-2021.168505
fatcat:mh6wxyxotfdz3i5d4eho3kxmxu
Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review
2020
Chaos, Solitons & Fractals
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a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. ...
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country ...
A deep learning algorithm is also used to classify patients based on 3D features of CT images. ...
doi:10.1016/j.chaos.2020.110338
pmid:33041533
pmcid:PMC7532790
fatcat:gl3i37hag5gflajsa7fh6khvva
Role of Artificial Intelligence in COVID-19 Detection
2021
Sensors
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We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. ...
Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21238045
pmid:34884045
fatcat:s6myy3c6dfbcje3356sqkcbtbu
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., and Inan, O.T., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre ...
Rubin, D. 2570-2579 Jiang, D., see 2473-2480 Jiang, H., see 2798-2805 Jiang, H., Yang, M., Chen, X., Li, M., Li, Y., and Wang, J., miRTMC: A miRNA Target Prediction Method Based on Matrix Completion ...
., +, JBHI Dec. 2020 3539-3550
Severity and Consolidation Quantification of COVID-19 From CT Images
Using Deep Learning Based on Hybrid Weak Labels. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
2021
Diagnostics
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Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. ...
For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. ...
COVLIAS 1.0: Inter-variability analysis of CT-based lung segmentation and quantification system for COVID-19 patients. ...
doi:10.3390/diagnostics11112025
pmid:34829372
pmcid:PMC8625039
fatcat:yjar5rkn3nejfoszpmb46abfcu
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
[article]
2020
arXiv
pre-print
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The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. ...
This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. ...
Joseph Paul Cohen from the University of Montreal for providing such a useful dataset of pneumonia images for the research community. ...
arXiv:2004.05835v3
fatcat:jxmnzemoznbuhpnqqkoqrnmcb4
SCOAT-Net: A Novel Network for Segmenting COVID-19 Lung Opacification from CT Images
[article]
2020
medRxiv
pre-print
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and healthy tissues, and the noise of CT images. ...
lung opacification from CT Images. ...
Artificial Intelligence for COVID-19 based on CT The segmentation of lung opacification based on CT images is an integral part of COVID-19 image processing, and there has been a series of related work. ...
doi:10.1101/2020.09.23.20191726
fatcat:wixzc3gejfgwtos3diujsfjsd4
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
2020
Computer Methods and Programs in Biomedicine
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The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. ...
This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. ...
Acknowledgments We thank the Brazilian Research Support Agencies: Coordination for the Improvement of Higher Education Personnel (CAPES), National Council for ...
doi:10.1016/j.cmpb.2020.105532
pmid:32446037
pmcid:PMC7207172
fatcat:ivp2e2vqyrfu7a6eizjfibg67a
COVID-19 Modeling: A Review
[article]
2021
arXiv
pre-print
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and deep machine learning, simulation modeling, social science methods, and hybrid modeling. ...
It constructs a research landscape of COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progress of modeling COVID-19 epidemic transmission ...
More information about COVID-19 modeling is in https://datasciences.org/covid19-modeling/. ...
arXiv:2104.12556v3
fatcat:pj2bketcrveafbjf2m7tx3odxy
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