{"id":"https://openalex.org/W2960473301","doi":"https://doi.org/10.1109/isbi.2019.8759474","title":"Localizing Image-Based Biomarker Regression Without Training Masks: A New Approach to Biomarker Discovery","display_name":"Localizing Image-Based Biomarker Regression Without Training Masks: A New Approach to Biomarker Discovery","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2960473301","doi":"https://doi.org/10.1109/isbi.2019.8759474","mag":"2960473301","pmid":"https://pubmed.ncbi.nlm.nih.gov/32454949"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7243964","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021632332","display_name":"Carlos Cano-Espinosa","orcid":"https://orcid.org/0000-0002-1392-9834"},"institutions":[{"id":"https://openalex.org/I130194489","display_name":"University of Alicante","ror":"https://ror.org/05t8bcz72","country_code":"ES","type":"education","lineage":["https://openalex.org/I130194489"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Carlos Cano-Espinosa","raw_affiliation_strings":["University of Alicante. Spain","University of Alicante, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alicante. Spain","institution_ids":["https://openalex.org/I130194489"]},{"raw_affiliation_string":"University of Alicante, Spain","institution_ids":["https://openalex.org/I130194489"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026386803","display_name":"Germ\u00e1n Gonz\u00e1lez","orcid":"https://orcid.org/0000-0001-9694-0766"},"institutions":[{"id":"https://openalex.org/I4210147298","display_name":"Sierra Engineering (United States)","ror":"https://ror.org/05kdrns38","country_code":"US","type":"company","lineage":["https://openalex.org/I4210147298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"German Gonzalez","raw_affiliation_strings":["Sierra Research SL. Spain","Sierra Research SL., Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sierra Research SL. Spain","institution_ids":["https://openalex.org/I4210147298"]},{"raw_affiliation_string":"Sierra Research SL., Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064775862","display_name":"George R. Washko","orcid":"https://orcid.org/0000-0002-2712-0569"},"institutions":[{"id":"https://openalex.org/I1283280774","display_name":"Brigham and Women's Hospital","ror":"https://ror.org/04b6nzv94","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283280774","https://openalex.org/I48633490"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George R. Washko","raw_affiliation_strings":["Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School. USA","Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School. USA","institution_ids":["https://openalex.org/I1283280774"]},{"raw_affiliation_string":"Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, USA","institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047085429","display_name":"Miguel Cazorlaa","orcid":null},"institutions":[{"id":"https://openalex.org/I130194489","display_name":"University of Alicante","ror":"https://ror.org/05t8bcz72","country_code":"ES","type":"education","lineage":["https://openalex.org/I130194489"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Miguel Cazorlaa","raw_affiliation_strings":["University of Alicante, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alicante, Spain","institution_ids":["https://openalex.org/I130194489"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002685368","display_name":"Ra\u00fal San Jo\u015be Est\u00e9par","orcid":"https://orcid.org/0000-0002-3677-1996"},"institutions":[{"id":"https://openalex.org/I1283280774","display_name":"Brigham and Women's Hospital","ror":"https://ror.org/04b6nzv94","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283280774","https://openalex.org/I48633490"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raul San Jose Estepar","raw_affiliation_strings":["Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School. USA","Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School. USA","institution_ids":["https://openalex.org/I1283280774"]},{"raw_affiliation_string":"Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, USA","institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2019","issue":null,"first_page":"679","last_page":"682"},"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":0.9993000030517578,"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":0.9993000030517578,"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":0.9973999857902527,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9959999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6876562237739563},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.6799547672271729},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5936092734336853},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5893478393554688},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.582915186882019},{"id":"https://openalex.org/keywords/imaging-biomarker","display_name":"Imaging biomarker","score":0.5641043186187744},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5065988302230835},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4755235016345978},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46017706394195557},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4516408443450928},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44331538677215576},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4223189651966095},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3202493190765381},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2110619843006134},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18943101167678833},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11000588536262512}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6876562237739563},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.6799547672271729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5936092734336853},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5893478393554688},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.582915186882019},{"id":"https://openalex.org/C45664433","wikidata":"https://www.wikidata.org/wiki/Q17029420","display_name":"Imaging biomarker","level":3,"score":0.5641043186187744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5065988302230835},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4755235016345978},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46017706394195557},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4516408443450928},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44331538677215576},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4223189651966095},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3202493190765381},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2110619843006134},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18943101167678833},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11000588536262512},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/isbi.2019.8759474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},{"id":"pmid:32454949","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32454949","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. IEEE International Symposium on Biomedical Imaging","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:7243964","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7243964","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Symp Biomed Imaging","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:7243964","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7243964","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Symp Biomed Imaging","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1876428122","display_name":null,"funder_award_id":"R21 HL140422","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G3953127214","display_name":null,"funder_award_id":"R01 HL116931","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G8371066574","display_name":null,"funder_award_id":"R01 HL116473","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"}],"funders":[{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2052771311","https://openalex.org/W2082907106","https://openalex.org/W2619094550","https://openalex.org/W2789563975","https://openalex.org/W2790168447","https://openalex.org/W2884402480","https://openalex.org/W2964275459","https://openalex.org/W6639824700","https://openalex.org/W6671162180","https://openalex.org/W6738991376"],"related_works":["https://openalex.org/W4221077608","https://openalex.org/W3173092539","https://openalex.org/W3197821184","https://openalex.org/W2999580839","https://openalex.org/W2983365766","https://openalex.org/W4288022876","https://openalex.org/W4366967093","https://openalex.org/W2547818937","https://openalex.org/W4402926319","https://openalex.org/W2157102420"],"abstract_inverted_index":{"Biomarker":[0],"inference":[1,171],"from":[2,78,103,176,294],"biomedical":[3],"images":[4],"is":[5,24,31,137,194,233,260,288],"one":[6],"of":[7,11,47,117,161,183,191,202,215,234,261,271],"the":[8,22,29,48,76,79,84,100,104,110,132,146,159,189,192,226,247,251,268],"main":[9],"tasks":[10],"medical":[12],"image":[13,80],"analysis.":[14],"Standard":[15],"techniques":[16,61],"follow":[17],"a":[18,44,64,72,120,128,140,173,181,199,212],"segmentation-and-measure":[19],"strategy,":[20],"where":[21],"structure":[23],"first":[25],"segmented":[26],"and":[27,127,148,166,198,208,219,237,274],"then":[28],"measurement":[30],"performed.":[32],"Recent":[33],"work":[34],"has":[35],"shown":[36],"that":[37,106,286],"such":[38,60],"strategy":[39],"could":[40],"be":[41],"replaced":[42],"by":[43],"direct":[45],"regression":[46,53,296],"biomarker":[49,77,111,133,193,295],"value":[50,190],"in":[51,158,172,257],"using":[52,139,278],"networks.":[54,297],"While":[55],"achieving":[56],"high":[57],"correlation":[58,213,249],"coefficients,":[59],"operate":[62],"as":[63],"'black-box',":[65],"not":[66,92,277],"offering":[67],"quality-control":[68],"images.":[69,178],"We":[70,154,179,210,283],"present":[71],"methodology":[73,90,157],"to":[74,108,123,151,186,225,290],"regress":[75],"while":[81],"simultaneously":[82],"computing":[83],"quality":[85],"control":[86],"image.":[87],"Our":[88],"proposed":[89,115],"does":[91],"require":[93],"segmentation":[94,121,265,279,292],"masks":[95,280,293],"for":[96,131,145,196,217,221,250,281],"training,":[97],"but":[98,253],"infers":[99],"segmentations":[101],"directly":[102],"pixels":[105],"used":[107],"compute":[109],"value.":[112],"The":[113,135,229],"network":[114,136],"consists":[116],"two":[118],"steps:":[119],"method":[122,130],"an":[124,149],"unknown":[125],"reference":[126,227],"summation":[129],"estimation.":[134],"optimized":[138],"dual":[141],"loss":[142],"function,":[143],"L2":[144],"biomarkers":[147,207,252],"L1":[150],"enforce":[152],"sparsity.":[153],"showcase":[155],"our":[156],"problem":[160],"pectoralis":[162],"muscle":[163],"area":[164,169],"(PMA)":[165,236],"subcutaneous":[167],"fat":[168],"(SFA)":[170],"single":[174],"slice":[175],"chest-CT":[177],"use":[180],"database":[182],"7000":[184],"cases":[185,204],"which":[187],"only":[188],"known":[195],"training":[197],"test":[200],"set":[201],"3000":[203],"with":[205,223,241],"both,":[206],"segmentations.":[209],"achieve":[211,246],"coefficient":[214,232],"0.97":[216],"PMA":[218],"0.98":[220],"SFA":[222],"respect":[224],"standard.":[228],"average":[230],"DICE":[231,255],"0.88":[235],"0.89":[238],"(SFA).":[239],"Comparing":[240],"standard":[242],"segment-and-measure":[243],"techniques,":[244],"we":[245,275],"same":[248],"smaller":[254],"coefficients":[256],"segmentation.":[258],"Such":[259],"little":[262],"surprise,":[263],"since":[264],"networks":[266],"are":[267,276],"upper":[269],"limit":[270],"performance":[272],"achievable,":[273],"training.":[282],"can":[284],"conclude":[285],"it":[287],"possible":[289],"infer":[291]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
