{"id":"https://openalex.org/W4414110759","doi":"https://doi.org/10.1109/jbhi.2025.3608153","title":"MediTEDNet: Visual State Space Model for Thyroid Eye Disease Classification","display_name":"MediTEDNet: Visual State Space Model for Thyroid Eye Disease Classification","publication_year":2025,"publication_date":"2025-09-10","ids":{"openalex":"https://openalex.org/W4414110759","doi":"https://doi.org/10.1109/jbhi.2025.3608153","pmid":"https://pubmed.ncbi.nlm.nih.gov/40928914"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2025.3608153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2025.3608153","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/A5006958609","display_name":"MC Han","orcid":"https://orcid.org/0009-0003-0287-9549"},"institutions":[{"id":"https://openalex.org/I43337087","display_name":"Hebei University","ror":"https://ror.org/01p884a79","country_code":"CN","type":"education","lineage":["https://openalex.org/I43337087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meihui Han","raw_affiliation_strings":["College of Mathematics and Information Sciences, Hebei University, Baoding, China","College of Mathematics and Information Sciences, Hebei University, Baoding, Hebei, China"],"raw_orcid":"https://orcid.org/0009-0003-0287-9549","affiliations":[{"raw_affiliation_string":"College of Mathematics and Information Sciences, Hebei University, Baoding, China","institution_ids":["https://openalex.org/I43337087"]},{"raw_affiliation_string":"College of Mathematics and Information Sciences, Hebei University, Baoding, Hebei, China","institution_ids":["https://openalex.org/I43337087"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junfen Chen","orcid":"https://orcid.org/0009-0003-7825-0771"},"institutions":[{"id":"https://openalex.org/I43337087","display_name":"Hebei University","ror":"https://ror.org/01p884a79","country_code":"CN","type":"education","lineage":["https://openalex.org/I43337087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfen Chen","raw_affiliation_strings":["College of Mathematics and Information Sciences, Hebei University, Baoding, China","College of Mathematics and Information Sciences, Hebei University, Baoding, Hebei, China"],"raw_orcid":"https://orcid.org/0009-0003-7825-0771","affiliations":[{"raw_affiliation_string":"College of Mathematics and Information Sciences, Hebei University, Baoding, China","institution_ids":["https://openalex.org/I43337087"]},{"raw_affiliation_string":"College of Mathematics and Information Sciences, Hebei University, Baoding, Hebei, China","institution_ids":["https://openalex.org/I43337087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072120458","display_name":"Mingyan Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166292","display_name":"Baoding People's Hospital","ror":"https://ror.org/0124z6a88","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210166292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyan Yao","raw_affiliation_strings":["Department of Endocrinology and Metabolism, Baoding No.1 Central Hospital, Baoding, China","Department of Endocrinology and Metabolism, Baoding No.1 Central Hospital, Baoding, Hebei, China"],"raw_orcid":"https://orcid.org/0000-0002-0392-2495","affiliations":[{"raw_affiliation_string":"Department of Endocrinology and Metabolism, Baoding No.1 Central Hospital, Baoding, China","institution_ids":["https://openalex.org/I4210166292"]},{"raw_affiliation_string":"Department of Endocrinology and Metabolism, Baoding No.1 Central Hospital, Baoding, Hebei, China","institution_ids":["https://openalex.org/I4210166292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108862466","display_name":"Bojun Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115299","display_name":"TED University","ror":"https://ror.org/0285rh439","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210115299"]},{"id":"https://openalex.org/I43337087","display_name":"Hebei University","ror":"https://ror.org/01p884a79","country_code":"CN","type":"education","lineage":["https://openalex.org/I43337087"]}],"countries":["CN","TR"],"is_corresponding":false,"raw_author_name":"Bojun Xie","raw_affiliation_strings":["College of Mathematics and Information Sciences, Hebei University, Baoding, China","College of Mathematics and Information Sciences, Hebei University, China"],"raw_orcid":"https://orcid.org/0009-0005-5064-0027","affiliations":[{"raw_affiliation_string":"College of Mathematics and Information Sciences, Hebei University, Baoding, China","institution_ids":["https://openalex.org/I43337087"]},{"raw_affiliation_string":"College of Mathematics and Information Sciences, Hebei University, China","institution_ids":["https://openalex.org/I43337087","https://openalex.org/I4210115299"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7322,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.8584036,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"30","issue":"3","first_page":"2550","last_page":"2562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9768999814987183,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9768999814987183,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5544999837875366},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5019000172615051},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4927999973297119},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4074999988079071},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3815000057220459},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.37940001487731934},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.35100001096725464},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.34860000014305115},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.34150001406669617}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739300012588501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7121999859809875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4927999973297119},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4074999988079071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39629998803138733},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.35100001096725464},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34200000762939453},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2978000044822693},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2784000039100647},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C2776148792","wikidata":"https://www.wikidata.org/wiki/Q3041498","display_name":"Eye disease","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2529999911785126}],"mesh":[{"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":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D041623","descriptor_name":"Tomography, Optical Coherence","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D041623","descriptor_name":"Tomography, Optical Coherence","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D049970","descriptor_name":"Graves Ophthalmopathy","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D049970","descriptor_name":"Graves Ophthalmopathy","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D049970","descriptor_name":"Graves Ophthalmopathy","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D049970","descriptor_name":"Graves Ophthalmopathy","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2025.3608153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2025.3608153","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:40928914","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40928914","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/G7880085846","display_name":null,"funder_award_id":"H2025201049","funder_id":"https://openalex.org/F4320322163","funder_display_name":"Natural Science Foundation of Hebei Province"}],"funders":[{"id":"https://openalex.org/F4320322163","display_name":"Natural Science Foundation of Hebei Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W948663339","https://openalex.org/W2013284550","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2554892747","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W3094820073","https://openalex.org/W3123751677","https://openalex.org/W3138516171","https://openalex.org/W3183105886","https://openalex.org/W3186410608","https://openalex.org/W4285593823","https://openalex.org/W4295096744","https://openalex.org/W4319812768","https://openalex.org/W4383741383","https://openalex.org/W4385245566","https://openalex.org/W4386076493","https://openalex.org/W4387212994","https://openalex.org/W4390421984","https://openalex.org/W4393320569","https://openalex.org/W4403342691","https://openalex.org/W4414243662","https://openalex.org/W6940546970"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Thyroid":[0],"eye":[1],"disease":[2],"(TED)":[3],"is":[4],"a":[5,106,140,160,166,174,209],"prevalent":[6],"autoimmune":[7],"orbital":[8],"disorder":[9],"that":[10,109,218],"can":[11],"severely":[12],"impair":[13],"visual":[14,115],"function":[15],"and":[16,54,92,114,124,135,146,158,206,208,230,237],"significantly":[17],"diminish":[18],"patients'":[19],"quality":[20],"of":[21,126,183,194,212],"life.":[22],"In":[23,129],"recent":[24],"years,":[25],"several":[26],"studies":[27],"have":[28],"attempted":[29],"to":[30,120,131,179],"automate":[31],"TED":[32,213],"diagnosis":[33],"using":[34,58],"optical":[35],"coherence":[36],"tomography":[37],"(OCT)":[38],"images.":[39,189,214],"However,":[40],"existing":[41,222],"approaches":[42],"primarily":[43],"rely":[44],"on":[45,144,201],"convolutional":[46],"neural":[47],"networks":[48],"(CNNs)":[49],"combined":[50],"with":[51,79],"attention":[52,112],"mechanisms":[53],"are":[55],"mostly":[56],"trained":[57],"traditional":[59],"cross-entropy":[60],"loss.":[61],"Although":[62],"Transformers":[63],"excel":[64],"at":[65],"modeling":[66],"long-range":[67],"dependencies,":[68],"their":[69,97],"quadratic":[70],"computational":[71],"complexity":[72],"when":[73],"processing":[74],"high-resolution":[75],"medical":[76],"images,":[77],"along":[78],"subpar":[80],"classification":[81],"accuracy":[82,229],"in":[83,187],"challenging":[84],"scenarios":[85],"such":[86],"as":[87],"highly":[88],"similar":[89],"pathological":[90],"regions":[91],"blurred":[93],"image":[94,127],"boundaries,":[95],"limit":[96],"clinical":[98,188,210],"applicability.":[99],"To":[100,190],"tackle":[101],"these":[102],"challenges,":[103],"we":[104,138,151,198],"propose":[105],"hybrid":[107],"architecture":[108],"integrates":[110],"CNNs,":[111],"mechanisms,":[113],"state":[116],"space":[117],"models":[118],"(VSSMs)":[119],"enhance":[121],"the":[122,181,192],"robustness":[123],"discriminability":[125],"features.":[128],"addition,":[130],"achieve":[132],"intra-class":[133],"compactness":[134],"inter-class":[136,154],"separation,":[137],"design":[139],"contrastive":[141],"loss":[142],"based":[143],"positive":[145],"negative":[147],"sample":[148],"prototypes.":[149],"Specifically,":[150],"introduce":[152,173],"proximal":[153],"mean":[155],"sampling":[156],"(PICMS)":[157],"incorporate":[159],"normalized":[161],"distance":[162],"metric":[163],"guided":[164],"by":[165],"distinguishable-indistinguishable":[167],"triplet":[168],"partitioning":[169],"mechanism.":[170],"We":[171],"also":[172],"hierarchical":[175],"noise-resilient":[176],"training":[177],"strategy":[178],"reduce":[180],"effects":[182],"noise":[184],"frequently":[185],"present":[186],"assess":[191],"effectiveness":[193],"our":[195,219],"proposed":[196],"model,":[197],"conduct":[199],"experiments":[200],"two":[202],"public":[203],"datasets":[204],"(OCT-2017":[205],"OCT-C8)":[207],"dataset":[211],"The":[215],"results":[216],"reveal":[217],"model":[220],"outperforms":[221],"methods":[223],"across":[224],"multiple":[225],"evaluation":[226],"metrics,":[227],"including":[228],"F1-score":[231],"while":[232],"demonstrating":[233],"superior":[234],"diagnostic":[235],"stability":[236],"generalization":[238],"capability.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-09T07:00:12.390032","created_date":"2025-10-10T00:00:00"}