{"id":"https://openalex.org/W4206720197","doi":"https://doi.org/10.1109/vcip53242.2021.9675332","title":"Learning to Detect Monoclonal Protein in Electrophoresis Images","display_name":"Learning to Detect Monoclonal Protein in Electrophoresis Images","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4206720197","doi":"https://doi.org/10.1109/vcip53242.2021.9675332"},"language":"en","primary_location":{"id":"doi:10.1109/vcip53242.2021.9675332","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675332","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","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/A5100755480","display_name":"Hanyu Li","orcid":"https://orcid.org/0000-0002-6676-1676"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanyu Li","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019680032","display_name":"Sabrina Racine\u2010Brzostek","orcid":"https://orcid.org/0000-0002-6296-6682"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sabrina Racine-Brzostek","raw_affiliation_strings":["Weill Cornell Medical College"],"affiliations":[{"raw_affiliation_string":"Weill Cornell Medical College","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010864350","display_name":"Nan Xi","orcid":"https://orcid.org/0000-0002-7334-7772"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nan Xi","raw_affiliation_strings":["University at Buffalo"],"affiliations":[{"raw_affiliation_string":"University at Buffalo","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110567111","display_name":"Jiwen Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140792","display_name":"Universidad Cat\u00f3lica Santo Domingo","ror":"https://ror.org/04ytq6y86","country_code":"DO","type":"education","lineage":["https://openalex.org/I4210140792"]}],"countries":["DO"],"is_corresponding":false,"raw_author_name":"Jiwen Luo","raw_affiliation_strings":["UCSD"],"affiliations":[{"raw_affiliation_string":"UCSD","institution_ids":["https://openalex.org/I4210140792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110837766","display_name":"Zhen Zhao","orcid":"https://orcid.org/0000-0001-6780-6576"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhen Zhao","raw_affiliation_strings":["Weill Cornell Medical College"],"affiliations":[{"raw_affiliation_string":"Weill Cornell Medical College","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085245110","display_name":"Junsong Yuan","orcid":"https://orcid.org/0000-0002-7901-8793"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junsong Yuan","raw_affiliation_strings":["University at Buffalo"],"affiliations":[{"raw_affiliation_string":"University at Buffalo","institution_ids":["https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100755480"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.3267,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70262097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.996399998664856,"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/T11688","display_name":"Microbial infections and disease research","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7306113243103027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6701170206069946},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5724629759788513},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5703275799751282},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5698316097259521},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.538682222366333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5203953981399536},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4603310823440552},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41996991634368896},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3561391234397888},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06986400485038757}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7306113243103027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6701170206069946},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5724629759788513},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5703275799751282},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5698316097259521},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.538682222366333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5203953981399536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4603310823440552},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41996991634368896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3561391234397888},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06986400485038757},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip53242.2021.9675332","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675332","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W168641031","https://openalex.org/W1974561441","https://openalex.org/W2000655497","https://openalex.org/W2036702941","https://openalex.org/W2101234009","https://openalex.org/W2127498532","https://openalex.org/W2149634568","https://openalex.org/W2159828572","https://openalex.org/W2160088903","https://openalex.org/W2164807675","https://openalex.org/W2325138686","https://openalex.org/W2919115771","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3193043704","https://openalex.org/W3171520305","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W3135818052","https://openalex.org/W2889302474"],"abstract_inverted_index":{"Monoclonal":[0],"protein":[1],"(M-protein)":[2],"detection":[3,98],"with":[4,115,136],"elec-trophoresis":[5],"is":[6,38,63,127,141],"of":[7,13,30,57,78,119,132,156,177],"vital":[8],"importance":[9],"for":[10,25,60,104,143,181],"the":[11,26,75,124,153,157,173,178],"diagnosis":[12,27],"lympho-proliferative":[14],"processes":[15],"and":[16,28,37,41,95,126,147,151,175],"monoclonal":[17],"gammopathies":[18],"(MGs).":[19],"Although":[20],"identifying":[21],"M-proteins":[22],"are":[23],"key":[24],"monitoring":[29],"these":[31,82],"disorders,":[32],"it":[33,50,140],"requires":[34,52],"specialized":[35],"knowledge":[36],"time":[39],"consuming":[40],"labor":[42],"intensive.":[43],"Despite":[44],"existing":[45],"powerful":[46],"machine":[47],"learning":[48],"methods,":[49],"often":[51],"to":[53,65,86,100,122,130,149],"obtain":[54],"large":[55],"number":[56],"labeled":[58,120],"data":[59,121],"training,":[61],"which":[62],"difficult":[64],"obtain.":[66],"Besides,":[67],"electrophoresis":[68,88],"image":[69,138],"quality":[70],"could":[71],"vary":[72],"dramatically,":[73],"affecting":[74],"proper":[76],"identification":[77],"M-protein.":[79],"To":[80],"address":[81],"challenges,":[83],"we":[84],"propose":[85],"represent":[87],"images":[89],"using":[90],"Gaussian":[91],"Mixture":[92],"Model":[93],"(GMM)":[94],"leverage":[96],"peak":[97],"method":[99,112,165],"identify":[101],"visual":[102],"features":[103],"M-protein":[105,182],"detection.":[106,183],"Utilizing":[107],"random":[108],"forest":[109],"classifier,":[110],"our":[111,163],"can":[113],"work":[114],"a":[116],"small":[117],"amount":[118],"train":[123],"model":[125,180],"not":[128],"sensitive":[129],"samples":[131],"varying":[133],"quality.":[134],"Furthermore,":[135],"extracted":[137],"features,":[139],"possible":[142],"specially":[144],"trained":[145],"technologists":[146],"pathologists":[148],"understand":[150],"check":[152],"decision":[154],"process":[155],"learned":[158],"model.":[159],"Extensive":[160],"experiments":[161],"indicate":[162],"proposed":[164,179],"achieves":[166],"satisfactory":[167],"results":[168],"on":[169],"test":[170],"data,":[171],"demonstrating":[172],"effectiveness":[174],"robustness":[176]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}