{"id":"https://openalex.org/W4412030625","doi":"https://doi.org/10.1109/jbhi.2025.3586054","title":"Medical Hyperspectral Image Feature Selection Framework Using Functional Data Analysis: Application to Membranous Nephropathy Pathologic Diagnosis","display_name":"Medical Hyperspectral Image Feature Selection Framework Using Functional Data Analysis: Application to Membranous Nephropathy Pathologic Diagnosis","publication_year":2025,"publication_date":"2025-07-04","ids":{"openalex":"https://openalex.org/W4412030625","doi":"https://doi.org/10.1109/jbhi.2025.3586054","pmid":"https://pubmed.ncbi.nlm.nih.gov/40614148"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2025.3586054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2025.3586054","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"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 Journal of Biomedical and Health Informatics","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/A5101697075","display_name":"Meng Lv","orcid":"https://orcid.org/0000-0002-2804-4342"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Lv","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2804-4342","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355122","display_name":"Shiyu Liu","orcid":"https://orcid.org/0000-0002-4857-1270"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyu Liu","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101448014","display_name":"Xiaoying Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoying Ma","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-9022-3555","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yue Yang","orcid":"https://orcid.org/0000-0003-1160-2528"},"institutions":[{"id":"https://openalex.org/I2801051648","display_name":"China-Japan Friendship Hospital","ror":"https://ror.org/037cjxp13","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2801051648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Yang","raw_affiliation_strings":["Department of Kidney Disease, China-Japan friendship hospital, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1160-2528","affiliations":[{"raw_affiliation_string":"Department of Kidney Disease, China-Japan friendship hospital, Beijing, China","institution_ids":["https://openalex.org/I2801051648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083504747","display_name":"Haihao Zhang","orcid":"https://orcid.org/0009-0009-7525-8384"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haihao Zhang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-7525-8384","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7015-7335","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0182,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77354055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"29","issue":"10","first_page":"7409","last_page":"7422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9340999722480774,"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":0.9340999722480774,"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/feature-selection","display_name":"Feature selection","score":0.6666746139526367},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6353899240493774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5843172669410706},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5676974058151245},{"id":"https://openalex.org/keywords/membranous-nephropathy","display_name":"Membranous nephropathy","score":0.546818733215332},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5412511229515076},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5361542105674744},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4961718022823334},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45554155111312866},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.44276943802833557},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4414594769477844},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35798847675323486},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3484073877334595},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.3098490536212921},{"id":"https://openalex.org/keywords/glomerulonephritis","display_name":"Glomerulonephritis","score":0.2402549684047699},{"id":"https://openalex.org/keywords/kidney","display_name":"Kidney","score":0.11955443024635315}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6666746139526367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6353899240493774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5843172669410706},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5676974058151245},{"id":"https://openalex.org/C2775946357","wikidata":"https://www.wikidata.org/wiki/Q1713037","display_name":"Membranous nephropathy","level":4,"score":0.546818733215332},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5412511229515076},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5361542105674744},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4961718022823334},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45554155111312866},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.44276943802833557},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4414594769477844},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35798847675323486},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3484073877334595},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.3098490536212921},{"id":"https://openalex.org/C2780368995","wikidata":"https://www.wikidata.org/wiki/Q605006","display_name":"Glomerulonephritis","level":3,"score":0.2402549684047699},{"id":"https://openalex.org/C2780091579","wikidata":"https://www.wikidata.org/wiki/Q9377","display_name":"Kidney","level":2,"score":0.11955443024635315},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007668","descriptor_name":"Kidney","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D007668","descriptor_name":"Kidney","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D007668","descriptor_name":"Kidney","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D007668","descriptor_name":"Kidney","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D007962","descriptor_name":"Leukocytes","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015433","descriptor_name":"Glomerulonephritis, Membranous","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D015433","descriptor_name":"Glomerulonephritis, Membranous","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D015433","descriptor_name":"Glomerulonephritis, Membranous","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D015433","descriptor_name":"Glomerulonephritis, Membranous","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":true},{"descriptor_ui":"D015433","descriptor_name":"Glomerulonephritis, Membranous","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2025.3586054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2025.3586054","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"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 Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:40614148","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40614148","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 journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2662217791","display_name":null,"funder_award_id":"62101040","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W16794263","https://openalex.org/W163215356","https://openalex.org/W1668085700","https://openalex.org/W1932531222","https://openalex.org/W1964505487","https://openalex.org/W1988386267","https://openalex.org/W2040870580","https://openalex.org/W2044184146","https://openalex.org/W2117533352","https://openalex.org/W2122111042","https://openalex.org/W2134603844","https://openalex.org/W2150566919","https://openalex.org/W2150990614","https://openalex.org/W2165232124","https://openalex.org/W2165835468","https://openalex.org/W2332583381","https://openalex.org/W2613070336","https://openalex.org/W2618004756","https://openalex.org/W2799954862","https://openalex.org/W2901920630","https://openalex.org/W2905565577","https://openalex.org/W2914205315","https://openalex.org/W2921862546","https://openalex.org/W2939307724","https://openalex.org/W2950325582","https://openalex.org/W2964323817","https://openalex.org/W2978620371","https://openalex.org/W2979945144","https://openalex.org/W2982154754","https://openalex.org/W2990124063","https://openalex.org/W3011934240","https://openalex.org/W3046819794","https://openalex.org/W3087334746","https://openalex.org/W3120077038","https://openalex.org/W3120808882","https://openalex.org/W3126361876","https://openalex.org/W3134985592","https://openalex.org/W3150214740","https://openalex.org/W3195184408","https://openalex.org/W3199672315","https://openalex.org/W3202024712","https://openalex.org/W4205462863","https://openalex.org/W4292854290","https://openalex.org/W4312112763","https://openalex.org/W4387802639","https://openalex.org/W4387803462","https://openalex.org/W4387849541","https://openalex.org/W4388665212","https://openalex.org/W4389577241","https://openalex.org/W4390604490","https://openalex.org/W4390617924"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2070598848","https://openalex.org/W2019190440","https://openalex.org/W3034864990","https://openalex.org/W2343470940"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,38,66,94,104],"core":[3],"issue":[4],"of":[5,40,99],"high-dimensional":[6],"data":[7,22,120],"processing":[8],"in":[9,65],"hyperspectral":[10,96],"pathological":[11,95],"diagnosis,":[12],"we":[13],"develop":[14],"a":[15,70],"new":[16],"feature":[17,67,85,114,141],"selection":[18,142],"framework":[19,26],"using":[20],"functional":[21,46,56],"analysis":[23],"(FSFDA).":[24],"The":[25],"models":[27],"pixel":[28],"spectra":[29],"as":[30],"continuous":[31],"functions":[32],"to":[33,61,83],"preserve":[34],"spectral":[35,51],"continuity,":[36],"overcoming":[37],"limitations":[39],"traditional":[41],"discrete":[42],"representations.":[43],"Based":[44],"on":[45,93],"features,":[47],"an":[48],"innovative":[49],"adaptive":[50,140],"segmentation":[52,64],"strategy":[53],"driven":[54],"by":[55,116],"change":[57],"rate":[58],"is":[59,81],"developed":[60,82],"achieve":[62],"optimal":[63],"space.":[68],"Additionally,":[69],"multi-criteria":[71],"scoring":[72],"mechanism":[73],"including":[74],"supervised":[75],"(FSFDA-S)":[76],"and":[77,125,144],"unsupervised":[78],"(FSFDA-U)":[79],"paradigms":[80],"enhance":[84],"diagnostic":[86,132],"discriminability":[87],"while":[88,112],"maintaining":[89],"sparsity.":[90],"Experimental":[91],"results":[92],"image":[97],"dataset":[98],"membranous":[100],"nephropathy":[101],"validate":[102],"that":[103],"proposed":[105],"method":[106],"achieves":[107],"over":[108],"99%":[109],"classification":[110],"accuracy":[111],"reducing":[113],"dimensions":[115],"94.5%.":[117],"For":[118],"cross-modal":[119],"involving":[121],"in-vivo":[122],"human":[123],"brain":[124],"white":[126],"blood":[127],"cells,":[128],"FSFDA":[129],"effectively":[130],"identifies":[131],"bands":[133],"aligned":[134],"with":[135],"histopathological":[136],"signatures,":[137],"verifying":[138],"its":[139],"ability":[143],"cross":[145],"sample":[146],"generalization":[147],"performance.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}