{"id":"https://openalex.org/W7126015320","doi":"https://doi.org/10.1109/bibm66473.2025.11357097","title":"Text-Driven Multi-Modal Prototype Optimization for Multi-Disease Recognition on Retinal Fundus Images","display_name":"Text-Driven Multi-Modal Prototype Optimization for Multi-Disease Recognition on Retinal Fundus Images","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126015320","doi":"https://doi.org/10.1109/bibm66473.2025.11357097"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5124278666","display_name":"Shuo Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuo Gao","raw_affiliation_strings":["Renmin University of China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124208660","display_name":"Gang Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Yang","raw_affiliation_strings":["Renmin University of China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124152732","display_name":"Yici Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yici Zhang","raw_affiliation_strings":["Renmin University of China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China,Beijing,China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124284908","display_name":"Jingjing Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101356","display_name":"Beijing Founder Electronics (China)","ror":"https://ror.org/00nwrzz95","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210101356"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Xu","raw_affiliation_strings":["Beijing Tiromu Medical Technology Co., Ltd.,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Tiromu Medical Technology Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210101356"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5124278666"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73258184,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3596","last_page":"3601"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9394000172615051,"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":0.9394000172615051,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0066999997943639755,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.005900000222027302,"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/discriminative-model","display_name":"Discriminative model","score":0.7857000231742859},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6679999828338623},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46970000863075256},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4212000072002411},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41130000352859497}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7857000231742859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.745199978351593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6704000234603882},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6679999828338623},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5321000218391418},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4212000072002411},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.26759999990463257}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.76362544298172,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-disease":[0],"recognition":[1],"is":[2,20],"an":[3],"important":[4],"task":[5],"in":[6],"retinal":[7],"fundus":[8],"images,":[9],"which":[10],"enables":[11,89],"efficient":[12,84],"and":[13,27,61,68,81,104],"low-cost":[14],"diagnosis":[15],"of":[16,30,78,92,125,131],"ophthalmic":[17],"disease.":[18],"It":[19],"still":[21],"challenging":[22],"due":[23],"to":[24,53],"the":[25,75,90,118,122],"difficulty":[26],"high":[28],"expense":[29],"labeling":[31],"medical":[32],"images.":[33],"To":[34],"tackle":[35],"this":[36],"problem,":[37],"we":[38],"introduce":[39],"a":[40,128],"Text-Driven":[41],"Multi-Modal":[42],"Prototype":[43],"Optimization":[44],"(TMPO)":[45],"approach,":[46],"based":[47],"on":[48,102],"CLIP.":[49],"By":[50],"leveraging":[51],"LLMs":[52],"generate":[54],"hierarchical":[55],"descriptions":[56],"for":[57],"multi-modal":[58,94],"training":[59],"pairs":[60],"establish":[62],"fine-grained":[63],"lesion":[64],"correspondences":[65],"across":[66],"visual":[67],"textual":[69],"representations,":[70],"our":[71,109],"TMPO":[72,116],"approach":[73,110],"enriches":[74],"semantic":[76],"information":[77],"multi-disease":[79],"labels":[80],"achieves":[82],"more":[83],"embedding":[85,98],"space":[86],"alignment.":[87],"This":[88],"generation":[91],"discriminative":[93],"prototypes":[95],"within":[96],"CLIP's":[97],"space.":[99],"Extensive":[100],"experiments":[101],"public":[103],"private":[105],"datasets":[106],"show":[107],"that":[108],"effectively":[111],"enhances":[112],"diagnostic":[113],"performance.":[114],"Specifically,":[115],"surpasses":[117],"SOTA":[119],"methods,":[120],"achieving":[121],"highest":[123],"mAP":[124],"0.7828":[126],"with":[127],"relative":[129],"improvement":[130],"5.04%.":[132],"Our":[133],"experiment":[134],"code":[135],"will":[136],"be":[137],"released":[138],"later.":[139]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
