{"id":"https://openalex.org/W3118460707","doi":"https://doi.org/10.1109/aipr47015.2019.9316541","title":"Segmentation of Infrared Breast Images Using MultiResUnet Neural Networks","display_name":"Segmentation of Infrared Breast Images Using MultiResUnet Neural Networks","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3118460707","doi":"https://doi.org/10.1109/aipr47015.2019.9316541","mag":"3118460707"},"language":"en","primary_location":{"id":"doi:10.1109/aipr47015.2019.9316541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr47015.2019.9316541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055042481","display_name":"Ange Lou","orcid":"https://orcid.org/0000-0002-3983-8877"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]},{"id":"https://openalex.org/I4210135078","display_name":"Washington University Medical Center","ror":"https://ror.org/036c27j91","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210135078"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ange Lou","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA","institution_ids":["https://openalex.org/I4210135078","https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020362391","display_name":"Shuyue Guan","orcid":"https://orcid.org/0000-0002-3779-9368"},"institutions":[{"id":"https://openalex.org/I4210135078","display_name":"Washington University Medical Center","ror":"https://ror.org/036c27j91","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210135078"]},{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuyue Guan","raw_affiliation_strings":["Department of Biomedical Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA","institution_ids":["https://openalex.org/I4210135078","https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082595255","display_name":"Nada Kamona","orcid":"https://orcid.org/0000-0002-4382-876X"},"institutions":[{"id":"https://openalex.org/I4210135078","display_name":"Washington University Medical Center","ror":"https://ror.org/036c27j91","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210135078"]},{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nada Kamona","raw_affiliation_strings":["Department of Biomedical Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA","institution_ids":["https://openalex.org/I4210135078","https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071064016","display_name":"Murray H. Loew","orcid":"https://orcid.org/0000-0002-1255-9341"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]},{"id":"https://openalex.org/I4210135078","display_name":"Washington University Medical Center","ror":"https://ror.org/036c27j91","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210135078"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Murray Loew","raw_affiliation_strings":["Department of Biomedical Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Medical Imaging and Image Analysis Laboratory, The George Washington University Medical Center, Washington, DC, USA","institution_ids":["https://openalex.org/I4210135078","https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055042481"],"corresponding_institution_ids":["https://openalex.org/I193531525","https://openalex.org/I4210135078"],"apc_list":null,"apc_paid":null,"fwci":0.3387,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.65689579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":1.0,"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/T12994","display_name":"Infrared Thermography in Medicine","score":1.0,"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/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9664999842643738,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7996666431427002},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.7183316349983215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6997323036193848},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6879684925079346},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6598064303398132},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.6297463178634644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5531467795372009},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5314551591873169},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.51357102394104},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5052679181098938},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4935113191604614},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4442993998527527},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4395831227302551},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43746885657310486},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.41518062353134155},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.23657995462417603},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15656235814094543},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14000269770622253}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7996666431427002},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.7183316349983215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6997323036193848},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6879684925079346},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6598064303398132},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.6297463178634644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5531467795372009},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5314551591873169},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.51357102394104},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5052679181098938},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4935113191604614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4442993998527527},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4395831227302551},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43746885657310486},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.41518062353134155},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.23657995462417603},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15656235814094543},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14000269770622253},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr47015.2019.9316541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr47015.2019.9316541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1729812787","https://openalex.org/W1901129140","https://openalex.org/W1984206642","https://openalex.org/W2024360923","https://openalex.org/W2062619189","https://openalex.org/W2078969925","https://openalex.org/W2097117768","https://openalex.org/W2135733203","https://openalex.org/W2142543201","https://openalex.org/W2167445191","https://openalex.org/W2167510172","https://openalex.org/W2194775991","https://openalex.org/W2911188335","https://openalex.org/W2928133111","https://openalex.org/W2944790770","https://openalex.org/W2953384591","https://openalex.org/W2964350391","https://openalex.org/W4237902716","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6681146042","https://openalex.org/W6684372118","https://openalex.org/W6694260854","https://openalex.org/W6713134421","https://openalex.org/W6760788157"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2752972570","https://openalex.org/W4297051394","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W4294770367"],"abstract_inverted_index":{"Breast":[0],"cancer":[1,18,27,43,159],"is":[2,19,47,236,239],"the":[3,12,55,59,68,77,99,140,193,200,210,222,231,246],"second":[4],"leading":[5],"cause":[6],"of":[7,16,54,82,121,147,234,245],"death":[8],"for":[9,41,70,132],"women":[10],"in":[11,102,105,152,192],"U.S.":[13],"Early":[14],"detection":[15],"breast":[17,26,42,56,62,100,141,148,158,255],"key":[20],"to":[21,25,39,96,125,138,189,203,253],"higher":[22,242],"survival":[23],"rates":[24],"patients.":[28],"We":[29,184],"are":[30],"investigating":[31],"infrared":[32,166],"(IR)":[33],"thermography":[34],"as":[35,73,75],"a":[36,113,129,145,186,250],"noninvasive":[37],"adjunct":[38],"mammography":[40],"screening.":[44],"IR":[45,63,103,149,256],"imaging":[46,157],"radiation-free,":[48],"pain-free,":[49],"and":[50,79,88,128,161,181,196,205,208,219],"non-contact.":[51],"Automatic":[52],"segmentation":[53,116],"area":[57,69,101,142],"from":[58,178,199],"acquired":[60,177],"full-size":[61],"images":[64,104,195,212,224,257],"will":[65],"help":[66],"limit":[67],"tumor":[71],"search,":[72],"well":[74],"reduce":[76],"time":[78],"effort":[80],"costs":[81],"manual":[83],"hand":[84],"segmentation.":[85],"Autoencoder-like":[86],"convolutional":[87],"deconvolutional":[89],"neural":[90],"networks":[91],"(C-DCNN)":[92],"had":[93],"been":[94],"applied":[95,112],"automatically":[97],"segment":[98,139,254],"previous":[106,260],"studies.":[107],"In":[108],"this":[109],"study,":[110],"we":[111,172],"state-of-the-art":[114],"deep-learning":[115],"model,":[117],"MultiResUnet,":[118],"which":[119,238],"consists":[120],"an":[122],"encoder":[123],"part":[124,131],"capture":[126],"features":[127],"decoder":[130],"precise":[133],"localization.":[134],"It":[135],"was":[136],"used":[137,173,185],"by":[143,156,225],"using":[144,215,226],"set":[146],"images,":[150,176],"collected":[151],"our":[153,165,259],"clinical":[154],"trials":[155],"patients":[160,180],"normal":[162],"volunteers":[163],"with":[164,221],"camera":[167],"(N2":[168],"Imager).":[169],"The":[170],"database":[171],"has":[174],"450":[175],"14":[179],"16":[182],"volunteers.":[183],"thresholding":[187],"method":[188],"remove":[190],"interference":[191],"raw":[194],"remapped":[197],"them":[198],"original":[201],"16-bit":[202],"8-bit,":[204],"then":[206],"cropped":[207],"segmented":[209],"8-bit":[211],"manually.":[213],"Experiments":[214],"leave-one-out":[216],"cross-validation":[217],"(LOOCV)":[218],"comparison":[220],"ground-truth":[223],"Tanimoto":[227],"similarity":[228],"show":[229],"that":[230,244],"average":[232],"accuracy":[233],"MultiResUnet":[235,248],"91.47%,":[237],"about":[240],"2%":[241],"than":[243,258],"autoencoder.":[247],"offers":[249],"better":[251],"approach":[252],"model.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
