{"id":"https://openalex.org/W4386634526","doi":"https://doi.org/10.1109/tmi.2023.3314507","title":"Deep Cascade-Learning Model via Recurrent Attention for Immunofixation Electrophoresis Image Analysis","display_name":"Deep Cascade-Learning Model via Recurrent Attention for Immunofixation Electrophoresis Image Analysis","publication_year":2023,"publication_date":"2023-09-12","ids":{"openalex":"https://openalex.org/W4386634526","doi":"https://doi.org/10.1109/tmi.2023.3314507","pmid":"https://pubmed.ncbi.nlm.nih.gov/37698964"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2023.3314507","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2023.3314507","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5103279220","display_name":"Xuming An","orcid":"https://orcid.org/0009-0004-3007-9781"},"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":false,"raw_author_name":"Xuming An","raw_affiliation_strings":["Department of Industrial Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-3007-9781","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102710989","display_name":"Pengchang Li","orcid":"https://orcid.org/0009-0002-7164-3313"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]},{"id":"https://openalex.org/I2801228662","display_name":"Peking Union Medical College Hospital","ror":"https://ror.org/04jztag35","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2801228662"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengchang Li","raw_affiliation_strings":["Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-7164-3313","affiliations":[{"raw_affiliation_string":"Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I200296433","https://openalex.org/I2801228662"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100374122","display_name":"Chen Zhang","orcid":"https://orcid.org/0000-0002-4767-9597"},"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":false,"raw_author_name":"Chen Zhang","raw_affiliation_strings":["Department of Industrial Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4767-9597","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3908,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55285751,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"42","issue":"12","first_page":"3847","last_page":"3859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9977999925613403,"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":0.9977999925613403,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.9793999791145325,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7523934841156006},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7431631088256836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7221848368644714},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6218733191490173},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5207757353782654},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5012145042419434},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.4836612641811371},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.475698322057724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44852158427238464},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21122178435325623}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7523934841156006},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7431631088256836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7221848368644714},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6218733191490173},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5207757353782654},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5012145042419434},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.4836612641811371},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.475698322057724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44852158427238464},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21122178435325623},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D004586","descriptor_name":"Electrophoresis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004586","descriptor_name":"Electrophoresis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004586","descriptor_name":"Electrophoresis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004586","descriptor_name":"Electrophoresis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tmi.2023.3314507","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2023.3314507","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:37698964","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37698964","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":"IEEE transactions on medical imaging","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3175690999","display_name":null,"funder_award_id":"9222014","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G410914050","display_name":null,"funder_award_id":"72271138","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7236899727","display_name":"\u5de5\u4e1a\u5927\u6570\u636e\u73af\u5883\u4e0b\u9762\u5411\u667a\u80fd\u5236\u9020\u7cfb\u7edf\u7684\u8d28\u91cf\u79d1\u5b66\u7ba1\u63a7\u65b9\u6cd5\u7814\u7a76","funder_award_id":"71932006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W168641031","https://openalex.org/W1641498739","https://openalex.org/W1974561441","https://openalex.org/W2083432437","https://openalex.org/W2119717200","https://openalex.org/W2159828572","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2526558307","https://openalex.org/W2581082771","https://openalex.org/W2803679599","https://openalex.org/W2810323699","https://openalex.org/W2914774348","https://openalex.org/W2963091558","https://openalex.org/W2963446712","https://openalex.org/W2964629181","https://openalex.org/W2981689412","https://openalex.org/W2982220924","https://openalex.org/W2998975547","https://openalex.org/W3009142496","https://openalex.org/W3028070348","https://openalex.org/W3085574514","https://openalex.org/W3112221804","https://openalex.org/W3128797821","https://openalex.org/W3139326342","https://openalex.org/W3196851488","https://openalex.org/W3211774562","https://openalex.org/W3212386989","https://openalex.org/W4206720197","https://openalex.org/W4289752563","https://openalex.org/W6628927728","https://openalex.org/W6637373629","https://openalex.org/W6682137061","https://openalex.org/W6763367864","https://openalex.org/W6803189269"],"related_works":["https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W1566155057","https://openalex.org/W2060986072","https://openalex.org/W2052574922","https://openalex.org/W64588465","https://openalex.org/W2795259429","https://openalex.org/W2005234362","https://openalex.org/W1997235926","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Immunofixation":[0],"Electrophoresis":[1],"(IFE)":[2],"analysis":[3],"has":[4],"been":[5],"an":[6,16,134],"indispensable":[7],"prerequisite":[8],"for":[9],"the":[10,57,70,75,87,92,104,149,154,161,176,222],"diagnosis":[11,30],"of":[12,28,46,79,94,157,178,224],"M-protein,":[13],"which":[14,44,122],"is":[15,53,195],"important":[17],"criterion":[18],"to":[19,37,103,142,198],"recognize":[20],"diversified":[21],"plasma":[22],"cell":[23],"diseases.":[24],"Existing":[25],"intelligent":[26],"methods":[27,213],"IFE":[29,83],"commonly":[31],"employ":[32],"a":[33,98,117,125],"single":[34],"unified":[35,51],"classifier":[36,127,136],"directly":[38],"classify":[39],"whether":[40],"M-protein":[41,47,71,88],"exists":[42],"and":[43,63,133,192,218],"isotype":[45,89,135],"is.":[48],"However,":[49],"this":[50,113],"classification":[52],"not":[54,173],"optimal":[55],"because":[56],"two":[58,105,145],"tasks":[59,106,146],"have":[60],"different":[61,65,228],"characteristics":[62],"require":[64],"feature":[66],"extraction":[67],"techniques.":[68],"Classifying":[69],"existence":[72],"depends":[73,90],"on":[74,91,129,138,214],"presence":[76],"or":[77],"absence":[78],"dense":[80,95,225],"bands":[81,226],"in":[82,227],"data,":[84],"while":[85],"classifying":[86],"location":[93],"bands.":[96],"Consequently,":[97],"cascading":[99],"two-classifier":[100],"framework":[101],"suitable":[102],"respectively":[107],"may":[108],"achieve":[109],"better":[110],"performance.":[111],"In":[112],"paper,":[114],"we":[115],"propose":[116],"novel":[118],"deep":[119,130,209],"cascade-learning":[120,210],"model,":[121],"sequentially":[123],"integrates":[124],"positive-negative":[126],"based":[128,137],"collocative":[131],"learning":[132],"recurrent":[139],"attention":[140,150,201],"model":[141],"address":[143],"these":[144],"respectively.":[147],"Specifically,":[148],"mechanism":[151],"can":[152,219],"mimic":[153],"visual":[155],"perception":[156],"clinicians,":[158],"where":[159],"only":[160,174],"most":[162],"informative":[163],"local":[164],"regions":[165,180],"are":[166],"extracted":[167],"through":[168],"sequential":[169],"partial":[170],"observations.":[171],"This":[172],"avoids":[175],"interference":[177],"redundant":[179],"but":[181],"also":[182,196],"saves":[183],"computational":[184],"power.":[185],"Further,":[186],"domain":[187],"knowledge":[188],"about":[189],"SP":[190],"lane":[191],"heavy-light-chain":[193],"lanes":[194],"introduced":[197],"assist":[199],"our":[200,208],"location.":[202],"Extensive":[203],"numerical":[204],"experiments":[205],"show":[206],"that":[207],"outperforms":[211],"state-of-the-art":[212],"recognized":[215],"evaluation":[216],"metrics":[217],"effectively":[220],"capture":[221],"co-location":[223],"lanes.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
