{"id":"https://openalex.org/W4288043247","doi":"https://doi.org/10.48550/arxiv.2207.10998","title":"Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction","display_name":"Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction","publication_year":2022,"publication_date":"2022-07-22","ids":{"openalex":"https://openalex.org/W4288043247","doi":"https://doi.org/10.48550/arxiv.2207.10998"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2207.10998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10998","pdf_url":"https://arxiv.org/pdf/2207.10998","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.10998","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055119015","display_name":"Pierre Raillard","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Raillard, Pierre","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069129755","display_name":"Lorenzo Cristoni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cristoni, Lorenzo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038584158","display_name":"Andrew T. Walden","orcid":"https://orcid.org/0000-0002-2466-929X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Walden, Andrew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054810128","display_name":"Roberto Lazzari","orcid":"https://orcid.org/0000-0003-4367-4075"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lazzari, Roberto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079892657","display_name":"Thomas Pulimood","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pulimood, Thomas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042365397","display_name":"Louis Grandjean","orcid":"https://orcid.org/0000-0002-1457-8327"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grandjean, Louis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037442059","display_name":"Claudia A. M. Gandini Wheeler\u2010Kingshott","orcid":"https://orcid.org/0000-0002-4832-1300"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wheeler-Kingshott, Claudia AM Gandini","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032309114","display_name":"Yipeng Hu","orcid":"https://orcid.org/0000-0003-4902-0486"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yipeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065017682","display_name":"Zachary M. C. Baum","orcid":"https://orcid.org/0000-0001-6838-335X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baum, Zachary MC","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5055119015"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11738","display_name":"Ultrasound in Clinical Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2706","display_name":"Critical Care and Intensive Care Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11738","display_name":"Ultrasound in Clinical Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2706","display_name":"Critical Care and Intensive Care Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7409828901290894},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7055822014808655},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.6153768301010132},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6026797294616699},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5958566069602966},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5728317499160767},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.536446213722229},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5152990818023682},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4742768704891205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3619900345802307},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3392401337623596},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.22534999251365662},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.15491849184036255},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.12500327825546265},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.0840006172657013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7409828901290894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7055822014808655},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.6153768301010132},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6026797294616699},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5958566069602966},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5728317499160767},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.536446213722229},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5152990818023682},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4742768704891205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3619900345802307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3392401337623596},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.22534999251365662},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.15491849184036255},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.12500327825546265},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0840006172657013},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2207.10998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10998","pdf_url":"https://arxiv.org/pdf/2207.10998","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10153273","is_oa":false,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10153273/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"     In:  Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (ASMUS: Advances in Simplifying Medical UltraSound).  (pp. pp. 1-11).  MICCAI (2022)    (In press).  ","raw_type":"Proceedings paper"},{"id":"doi:10.48550/arxiv.2207.10998","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2207.10998","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2207.10998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10998","pdf_url":"https://arxiv.org/pdf/2207.10998","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.550000011920929,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2770234245","https://openalex.org/W96612179","https://openalex.org/W4229499248","https://openalex.org/W2566006169","https://openalex.org/W2987774938","https://openalex.org/W4256492088","https://openalex.org/W632915154","https://openalex.org/W2055733372","https://openalex.org/W3022067003","https://openalex.org/W4378874356"],"abstract_inverted_index":{"Artificial":[0],"intelligence-based":[1],"analysis":[2,42],"of":[3,69,73,87,91,104,110,122,133,141,150,183],"lung":[4],"ultrasound":[5],"imaging":[6],"has":[7],"been":[8],"demonstrated":[9],"as":[10,52,100,125],"an":[11,148],"effective":[12,131],"technique":[13],"for":[14,56,178,187,197],"rapid":[15,179],"diagnostic":[16],"decision":[17],"support":[18],"throughout":[19],"the":[20,101,120,130,176],"COVID-19":[21,198],"pandemic.":[22,106],"However,":[23],"such":[24,99,184],"techniques":[25],"can":[26,146],"require":[27],"days-":[28],"or":[29,96],"weeks-long":[30],"training":[31,62,142],"processes":[32],"and":[33,75,159,164,181,192,200],"hyper-parameter":[34],"tuning":[35],"to":[36,79,168],"develop":[37],"intelligent":[38],"deep":[39,53],"learning":[40],"image":[41],"models.":[43],"This":[44,85],"work":[45],"focuses":[46],"on":[47,82,153],"leveraging":[48],"'off-the-shelf'":[49],"pre-trained":[50,67],"models":[51],"feature":[54,126],"extractors":[55,127],"scoring":[57],"disease":[58],"severity":[59,136,156],"with":[60],"minimal":[61],"time.":[63,143],"We":[64],"propose":[65],"using":[66],"initializations":[68],"existing":[70,123],"methods":[71,124,145,186],"ahead":[72],"simple":[74],"compact":[76],"neural":[77],"networks":[78],"reduce":[80],"reliance":[81],"computational":[83,88],"capacity.":[84],"reduction":[86],"capacity":[89],"is":[90],"critical":[92],"importance":[93],"in":[94,129,194,202],"time-limited":[95],"resource-constrained":[97],"circumstances,":[98],"early":[102],"stages":[103],"a":[105,108,154],"On":[107],"dataset":[109],"49":[111],"patients,":[112,199],"comprising":[113],"over":[114,151],"20,000":[115],"images,":[116],"we":[117],"demonstrate":[118,175],"that":[119],"use":[121,182],"results":[128,174],"classification":[132],"COVID-19-related":[134],"pneumonia":[135],"while":[137],"requiring":[138],"only":[139],"minutes":[140],"Our":[144],"achieve":[147],"accuracy":[149],"0.93":[152],"4-level":[155],"score":[157],"scale":[158],"provides":[160],"comparable":[161],"per-patient":[162],"region":[163],"global":[165],"scores":[166],"compared":[167],"expert":[169],"annotated":[170],"ground":[171],"truths.":[172],"These":[173],"capability":[177],"deployment":[180],"minimally-adapted":[185],"progress":[188],"monitoring,":[189],"patient":[190],"stratification":[191],"management":[193],"clinical":[195],"practice":[196],"potentially":[201],"other":[203],"respiratory":[204],"diseases.":[205]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}