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Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies [article]

Leo K. Tam, Xiaosong Wang, Evrim Turkbey, Kevin Lu, Yuhong Wen, Daguang Xu
<span title="2020-07-31">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Along with the dataset, we present a joint vision language weakly supervised transformer layer-selected one-stage dual head detection architecture (LITERATI) alongside strong baseline comparisons with  ...  We present a challenging new set of radiologist paired bounding box and natural language annotations on the publicly available MIMIC-CXR dataset especially focussed on pneumonia and pneumothorax.  ...  Conclusion We present a weakly supervised vision language method and associated clinical referring expressions dataset on pneumonia and pneumothorax chest x-ray images at scale.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.15778v1">arXiv:2007.15778v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wrbt25rzanhprn3eniqv77dvki">fatcat:wrbt25rzanhprn3eniqv77dvki</a> </span>
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Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing [article]

Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay
<span title="2022-05-17">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Further, we propose a self-supervised joint vision--language approach with a focus on better text modelling.  ...  Biomedical text with its complex semantics poses additional challenges in vision-language modelling compared to the general domain, and previous work has used insufficiently adapted models that lack domain-specific  ...  This article focuses on self-supervised vision-language processing (VLP) for paired image and text data in the biomedical domain.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.09817v2">arXiv:2204.09817v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wxrlara2wfhotppk4lsbvrvtuy">fatcat:wxrlara2wfhotppk4lsbvrvtuy</a> </span>
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Screening of Radiological Images Suspected of Containing Lung Nodules

Raúl Pedro Aceñero Eixarch, Raúl Díaz-Usechi Laplaza, Rafael Berlanga Llavori
<span title="">2022</span> <i title="IGI Global"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ygjmjirlvzduxihrunnznzozny" style="color: black;">International Journal of Computer Vision and Image Processing</a> </i> &nbsp;
Results over existing datasets show that, contrary to other diseases like pneumonia, detecting nodules is a hard task when using only radiographies.  ...  This paper presents a study about screening large radiological image streams produced in hospitals for earlier detection of lung nodules.  ...  ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4018/ijcvip.20220101.oa1">doi:10.4018/ijcvip.20220101.oa1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sjhhofa3cbe6tfngceqseyotzq">fatcat:sjhhofa3cbe6tfngceqseyotzq</a> </span>
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Learning to Localize Objects Using Limited Annotation, With Applications to Thoracic Diseases

Eyal Rozenberg, Daniel Freedman, Alex A. Bronstein
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INTRODUCTION L ARGE-SCALE labelled datasets are one of the key ingredients in many recent algorithms in image processing and computer vision.  ...  ; and weakly supervised data rather than fully supervised data.  ...  His research interests include numerical geometry, computer vision, and machine learning. Prof.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3075555">doi:10.1109/access.2021.3075555</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/22q6dyzbgfbjjhae27hlbmskla">fatcat:22q6dyzbgfbjjhae27hlbmskla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717202825/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09416490.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4a/40/4a402d02afc29bd27055eb3e9b312ca79ea5bc3a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3075555"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification [article]

Ajay Jaiswal, Liyan Tang, Meheli Ghosh, Justin Rousseau, Yifan Peng, Ying Ding
<span title="2021-11-19">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Extracting pathologic findings and diagnoses from radiology reports is important for quality control, population health, and monitoring of disease progress.  ...  We introduce RadBERT-CL, which fuses these information into BlueBert via a self-supervised contrastive loss.  ...  Classifying radiology reports can help to identify patient cohorts and enable precision medicine on a large scale.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.15426v2">arXiv:2110.15426v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wfura5vrk5gt3cjwq2epweibti">fatcat:wfura5vrk5gt3cjwq2epweibti</a> </span>
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Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions [article]

Li Yao, Jordan Prosky, Eric Poblenz, Ben Covington, Kevin Lyman
<span title="2018-03-21">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
using only image-level labels.  ...  We introduce an approach to managing these practical constraints by applying a novel architecture which learns at multiple resolutions while generating saliency maps with weak supervision.  ...  [3] used a similar approach to ours in order to detect and spatially locate thoracic diseases.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.07703v1">arXiv:1803.07703v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/imw2fo7p7vfnfejvvhxpnf7juy">fatcat:imw2fo7p7vfnfejvvhxpnf7juy</a> </span>
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Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Chaochao Yan, Jiawen Yao, Ruoyu Li, Zheng Xu, Junzhou Huang
<span title="">2018</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7y7p3qrae5bnlok65yvbqtexc4" style="color: black;">Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB &#39;18</a> </i> &nbsp;
In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as  ...  While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic  ...  In this paper, we proposed a unified weakly-supervised deep learning framework to jointly perform thoracic disease classification and localization on chest X-rays only using noisy multi-class disease label  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3233547.3233573">doi:10.1145/3233547.3233573</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bcb/YanYL0H18.html">dblp:conf/bcb/YanYL0H18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rjwwuckkz5dktj6u6mct73lfhi">fatcat:rjwwuckkz5dktj6u6mct73lfhi</a> </span>
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Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks [article]

Rachel Lea Draelos, Lawrence Carin
<span title="2021-11-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Experiments on PASCAL VOC 2012, including crowd-sourced evaluations, illustrate that while HiResCAM's explanations faithfully reflect the model, Grad-CAM often expands the attention to create bigger and  ...  We prove that HiResCAM is a generalization of CAM and explore the relationships between HiResCAM and other gradient-based explanation methods.  ...  The authors also thank Paidamoyo Chapfuwa, PhD, for thoughtful comments on the manuscript, David Dov, PhD, for discussion of multiple instance learning, and Geoffrey D.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.08891v4">arXiv:2011.08891v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yo6gawyxdraedbe3nmwnwzvxu4">fatcat:yo6gawyxdraedbe3nmwnwzvxu4</a> </span>
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Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
<span title="2020-07-28">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lvjqpr55r5fcfpfbeu7qp2jwgm" style="color: black;">Machine Vision and Applications</a> </i> &nbsp;
, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections.  ...  Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials.  ...  A large-scale dataset was used (MIMIC-CXR) and highest AUC recorded for multi-class severity classification was for DenseNet w/ Semi-Supervised II (81.3%), as well as for no-pneumonia and mild pneumonia  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00138-020-01101-5">doi:10.1007/s00138-020-01101-5</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32834523">pmid:32834523</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7386599/">pmcid:PMC7386599</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tkkylrptc5hkpoj52hjs3kuttu">fatcat:tkkylrptc5hkpoj52hjs3kuttu</a> </span>
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Localization with Limited Annotation for Chest X-rays [article]

Eyal Rozenberg, Daniel Freedman, Alex Bronstein
<span title="2019-10-10">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We apply our technique to the localization of thoracic diseases in chest X-ray images and demonstrate state-of-the-art localization performance on the ChestX-ray14 dataset.  ...  We propose a novel loss function for tackling this problem; the loss is a continuous relaxation of a well-defined discrete formulation of weakly supervised learning and is numerically well-posed.  ...  Our method is based on a novel loss function, which is mathematically and numerically well-posed; and an architecture that explicitly accounts for patch non-independence and shift-invariance.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.08842v2">arXiv:1909.08842v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/smjzps543retvkxxo3s3cvufni">fatcat:smjzps543retvkxxo3s3cvufni</a> </span>
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Clinically Accurate Chest X-Ray Report Generation [article]

Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi
<span title="2019-07-29">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A number of prior works have focused on this problem, employing advanced methods from computer vision and natural language generation to produce readable reports.  ...  We verify this system on two datasets, Open-I and MIMIC-CXR, and demonstrate that our model offers marked improvements on both language generation metrics and CheXpert assessed accuracy over a variety  ...  Marzyeh Ghassemi is partially funded by a CIFAR AI Chair at the Vector Institute, and an NSERC Discovery Grant.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.02633v2">arXiv:1904.02633v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jcot36p3vbcmzakq5hqyhpxnzq">fatcat:jcot36p3vbcmzakq5hqyhpxnzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200923210857/https://arxiv.org/pdf/1904.02633v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/40/0f/400f03226717a292fdc31696efc4d3257026dd72.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.02633v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [article]

Rachel Lea Draelos, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, Lawrence Carin
<span title="2020-02-17">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities.  ...  We also developed a model for multi-organ, multi-disease classification of chest CT volumes that uses a deep convolutional neural network (CNN).  ...  Acknowledgements We would like to thank Mark Martin, the Duke University Office of Information Technology (OIT), and the Duke Protected Analytics Computing Environment (PACE) team.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.04752v2">arXiv:2002.04752v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/acvabjkembhevjljjex2rq5n5y">fatcat:acvabjkembhevjljjex2rq5n5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321163043/https://arxiv.org/ftp/arxiv/papers/2002/2002.04752.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.04752v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
<span title="2021-04-06">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a> </i> &nbsp;
DLA has been widely used in medical imaging to detect the presence or absence of the disease.  ...  It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA.  ...  4339 cancer cases) DIB-MG DIB-MG is weakly supervised learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-021-10707-4">doi:10.1007/s11042-021-10707-4</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33841033">pmid:33841033</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8023554/">pmcid:PMC8023554</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cm522go4nbdbnglgzpw4nu7tbi">fatcat:cm522go4nbdbnglgzpw4nu7tbi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718101016/https://link.springer.com/content/pdf/10.1007/s11042-021-10707-4.pdf?error=cookies_not_supported&amp;code=ba67d511-a3dc-4184-9895-59b7f0023263" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/23/a323533f5f50cfce949d47caac97f791cdf1bf92.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-021-10707-4"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023554" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A Cascade‐SEME network for COVID‐19 detection in chest c‐ray images

Dailin Lv, Yaqi Wang, Shuai Wang, Qianni Zhang, Wuteng Qi, Yunxiang Li, Lingling Sun
<span title="2021-03-29">2021</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4xxxfsh2pjc4nhsa3qijpqv6zy" style="color: black;">Medical Physics (Lancaster)</a> </i> &nbsp;
The visual interpretation step using Grad-CAM finds that the region of attention on radiographs of different types of pneumonia are located in different regions of the lungs.  ...  Three datasets were used: a set of Chest x-ray Images for Classification with bacterial pneumonia, viral pneumonia and normal chest x-rays, a COVID chest x-ray dataset with COVID-19, and a Lung Segmentation  ...  The authors would like to thank Giussepi for providing language help and Zhihua Huang for providing chart processing help during this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/mp.14711">doi:10.1002/mp.14711</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33778966">pmid:33778966</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nnwgdfcpvrgvjjuegouqhawil4">fatcat:nnwgdfcpvrgvjjuegouqhawil4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715093413/https://aapm.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.14711" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f5/18/f518828f75fd64a93e1730490f0b919bae003dad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/mp.14711"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> wiley.com </button> </a>

Geriatric Emergency Medicine

Amal Mattu
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4un6y2n6d5az5mvih5kfc6xjyi" style="color: black;">Emergency medicine clinics of North America</a> </i> &nbsp;
The elderly are also well known to present with atypical presentations of common disease, including myocardial infarction, pneumonia, urinary tract infection, appendicitis, and shock.  ...  The medical community is now starting to understand the significant changes that occur with aging that produce altered disease conditions and presentations in elderly patients.  ...  Acknowledgments The author thanks Fiona Abbott, Jeantel Degazon, Laurie Sickles and Clifford Knapp for their assistance in the preparation of this manuscript.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.emc.2016.06.002">doi:10.1016/j.emc.2016.06.002</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27475022">pmid:27475022</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mjnvvua4p5a3tbpxvpo5ggi5am">fatcat:mjnvvua4p5a3tbpxvpo5ggi5am</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706014430/http://blog.utp.edu.co/internaumana/files/2010/10/Geriatric-Neurologic-Emergencies.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/34/80/3480b7e399cf731f6f449a509ec15d5ff035608b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.emc.2016.06.002"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>
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