{"id":"https://openalex.org/W3000885462","doi":"https://doi.org/10.1117/12.2551106","title":"Weakly-supervised lesion segmentation on CT scans using co-segmentation","display_name":"Weakly-supervised lesion segmentation on CT scans using co-segmentation","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3000885462","doi":"https://doi.org/10.1117/12.2551106","mag":"3000885462"},"language":"en","primary_location":{"id":"doi:10.1117/12.2551106","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2551106","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2001.08590","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029424334","display_name":"Vatsal Agarwal","orcid":"https://orcid.org/0009-0004-9470-198X"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vatsal Agarwal","raw_affiliation_strings":["National Institutes of Health (United States)","National Institutes of Health UNITED STATES"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]},{"raw_affiliation_string":"National Institutes of Health UNITED STATES","institution_ids":["https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081658414","display_name":"Youbao Tang","orcid":"https://orcid.org/0000-0001-8719-3375"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Youbao Tang","raw_affiliation_strings":["National Institutes of Health (United States)","National Institutes of Health UNITED STATES"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]},{"raw_affiliation_string":"National Institutes of Health UNITED STATES","institution_ids":["https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016038454","display_name":"Jing Xiao","orcid":"https://orcid.org/0000-0001-9615-4749"},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xiao","raw_affiliation_strings":["Ping An Technology Co., Ltd. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology Co., Ltd. (China)","institution_ids":["https://openalex.org/I4401726822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016047550","display_name":"Ronald M. Summers","orcid":"https://orcid.org/0000-0001-8081-7376"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ronald M. Summers","raw_affiliation_strings":["National Institutes of Health (United States)","National Institutes of Health UNITED STATES"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]},{"raw_affiliation_string":"National Institutes of Health UNITED STATES","institution_ids":["https://openalex.org/I1299303238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1175,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47424478,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"51","last_page":"51"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9986000061035156,"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/T10862","display_name":"AI in cancer detection","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8903526067733765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7044497132301331},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6544232368469238},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.654194712638855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6525452733039856},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5863345861434937},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5053444504737854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.502556562423706},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3548504114151001},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2835742235183716},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.0762915313243866}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8903526067733765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7044497132301331},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6544232368469238},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.654194712638855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6525452733039856},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5863345861434937},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5053444504737854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.502556562423706},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3548504114151001},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2835742235183716},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0762915313243866}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1117/12.2551106","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2551106","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Computer-Aided Diagnosis","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2001.08590","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.08590","pdf_url":"https://arxiv.org/pdf/2001.08590","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":"doi:10.48550/arxiv.2001.08590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2001.08590","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"},{"id":"mag:3000885462","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2001.08590","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.08590","pdf_url":"https://arxiv.org/pdf/2001.08590","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":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337376","display_name":"NIH Clinical Center","ror":"https://ror.org/04vfsmv21"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3000885462.pdf"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W1909740415","https://openalex.org/W2124351162","https://openalex.org/W2161236525","https://openalex.org/W2168804568","https://openalex.org/W2194775991","https://openalex.org/W2295160225","https://openalex.org/W2412782625","https://openalex.org/W2552414813","https://openalex.org/W2792010009","https://openalex.org/W2809942416","https://openalex.org/W2810849947","https://openalex.org/W2883245728","https://openalex.org/W2883683269","https://openalex.org/W2896011443","https://openalex.org/W2899771611","https://openalex.org/W2921110842","https://openalex.org/W2921612408","https://openalex.org/W2962878449","https://openalex.org/W2963008565","https://openalex.org/W2963243407","https://openalex.org/W2963420686","https://openalex.org/W2963663752","https://openalex.org/W2964216718","https://openalex.org/W2964283970","https://openalex.org/W2979564444","https://openalex.org/W2980311701","https://openalex.org/W3027427064"],"related_works":["https://openalex.org/W3011742499","https://openalex.org/W3027427064","https://openalex.org/W2784514662","https://openalex.org/W3158700092","https://openalex.org/W3028437378","https://openalex.org/W3207213789","https://openalex.org/W2755676576","https://openalex.org/W3174722556","https://openalex.org/W3045983564","https://openalex.org/W2979716057","https://openalex.org/W3164640030","https://openalex.org/W3157139923","https://openalex.org/W2416223209","https://openalex.org/W3162726628","https://openalex.org/W2295042533","https://openalex.org/W3048842081","https://openalex.org/W3008812219","https://openalex.org/W2728648333","https://openalex.org/W2895582358","https://openalex.org/W3036480566"],"abstract_inverted_index":{"Lesion":[0],"segmentation":[1,26,105,170],"on":[2,38,156],"computed":[3],"tomography":[4],"(CT)":[5],"scans":[6],"is":[7,21,27,138],"an":[8,39,134],"important":[9],"step":[10],"for":[11,86],"precisely":[12],"monitoring":[13],"changes":[14],"in":[15,46,64],"lesion/tumor":[16],"growth.":[17],"This":[18],"task,":[19],"however,":[20],"very":[22],"challenging":[23],"since":[24],"manual":[25],"prohibitively":[28],"timeconsuming,":[29],"expensive,":[30],"and":[31,68,118,126],"requires":[32],"professional":[33],"knowledge.":[34],"Current":[35],"practices":[36],"rely":[37],"imprecise":[40],"substitute":[41],"called":[42],"response":[43],"evaluation":[44],"criteria":[45],"solid":[47],"tumors":[48],"(RECIST).":[49],"Although":[50],"these":[51,73],"markers":[52,74],"lack":[53],"detailed":[54],"information":[55],"about":[56,177],"the":[57,76,110,115,128,157,163,173],"lesion":[58,88,104,112,124,169],"regions,":[59],"they":[60],"are":[61],"commonly":[62],"found":[63],"hospitals\u2019":[65],"picture":[66],"archiving":[67],"communication":[69],"systems":[70],"(PACS).":[71],"Thus,":[72],"have":[75],"potential":[77],"to":[78,122,141,144,181],"serve":[79],"as":[80],"a":[81,97,150],"powerful":[82],"source":[83],"of":[84,152],"weak-supervision":[85],"2D":[87],"segmentation.":[89],"To":[90],"approach":[91,166],"this":[92,94,132],"problem,":[93],"paper":[95],"proposes":[96],"convolutional":[98],"neural":[99],"network":[100],"(CNN)":[101],"based":[102],"weakly-supervised":[103],"method,":[106],"which":[107],"first":[108],"generates":[109],"initial":[111,129],"masks":[113],"from":[114,149],"RECIST":[116],"measurements":[117],"then":[119],"utilizes":[120],"co-segmentation":[121,136,165],"leverage":[123],"similarities":[125],"refine":[127],"masks.":[130],"In":[131],"work,":[133],"attention-based":[135],"model":[137],"adopted":[139],"due":[140],"its":[142],"ability":[143],"learn":[145],"more":[146],"discriminative":[147],"features":[148],"pair":[151],"images.":[153],"Experimental":[154],"results":[155],"NIH":[158],"DeepLesion":[159],"dataset":[160],"demonstrate":[161],"that":[162],"proposed":[164],"significantly":[167],"improves":[168],"performance,":[171],"e.g":[172],"Dice":[174],"score":[175],"increases":[176],"4.0%":[178],"(from":[179],"85.8%":[180],"89.8%).":[182]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}