{"id":"https://openalex.org/W4408223428","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906113","title":"Deep Learning-Based Intelligent Aided Diagnosis System for Ophthalmic Ultrasound Images","display_name":"Deep Learning-Based Intelligent Aided Diagnosis System for Ophthalmic Ultrasound Images","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4408223428","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906113"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei64163.2024.10906113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5033437595","display_name":"Zemeng Li","orcid":null},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zemeng Li","raw_affiliation_strings":["Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College,Tianjin,China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029584046","display_name":"Sheng Zhou","orcid":"https://orcid.org/0000-0003-3578-7926"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Zhou","raw_affiliation_strings":["Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College,Tianjin,China","institution_ids":["https://openalex.org/I200296433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I200296433"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.8833000063896179,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.8833000063896179,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6640726327896118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6136618852615356},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5128705501556396},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.463786244392395},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4255371689796448},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2471698522567749},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2073880434036255}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6640726327896118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6136618852615356},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5128705501556396},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.463786244392395},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4255371689796448},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2471698522567749},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2073880434036255}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei64163.2024.10906113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2014965151","display_name":null,"funder_award_id":"22JCYBJC00340","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"}],"funders":[{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2021597812","https://openalex.org/W2777186991","https://openalex.org/W2907620831","https://openalex.org/W2910515580","https://openalex.org/W2950829400","https://openalex.org/W2969677637","https://openalex.org/W2989607730","https://openalex.org/W2995942064","https://openalex.org/W2996367417","https://openalex.org/W3017141460","https://openalex.org/W3024991695","https://openalex.org/W3081802893","https://openalex.org/W3083622693","https://openalex.org/W3086928989","https://openalex.org/W3091187282","https://openalex.org/W3121313310","https://openalex.org/W3156906645","https://openalex.org/W3174025757","https://openalex.org/W3194970959","https://openalex.org/W3196684964","https://openalex.org/W3204496551","https://openalex.org/W4220728532"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","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"],"abstract_inverted_index":{"Objective:":[0],"To":[1],"design":[2],"an":[3,21,186,200],"intelligent":[4,22,128,207,231],"assisted":[5,129],"diagnosis":[6,130,175,179,209],"system":[7,180,210],"for":[8,28,60,211],"ophthalmic":[9,69,79,125,225,234],"ultrasound":[10,70,80,126,193,213,226],"images":[11,71,194],"based":[12],"on":[13,156],"a":[14,116],"deep":[15,205],"learning":[16],"approach,":[17],"which":[18,215],"serves":[19],"as":[20],"clinical":[23,174,235],"diagnostic":[24],"aid":[25],"analysis":[26],"tool":[27],"posterior":[29],"ocular":[30,192,212],"segment":[31],"diseases.":[32],"Methods:":[33],"In":[34],"order":[35,75],"to":[36,50,76],"accomplish":[37],"multi-level":[38],"feature":[39,97,154],"extraction":[40],"and":[41,66,99,148,164,184,196,218],"fusion,":[42],"two":[43],"pre-trained":[44],"network":[45],"models-InceptionV3-Xception":[46],"-":[47],"are":[48],"concatenated":[49],"create":[51],"the":[52,61,102,111,124,135,151,157,230],"InceptionV3-Xception":[53],"fusion":[54,142],"model.":[55],"A":[56],"classifier":[57],"better":[58],"suited":[59],"task":[62],"of":[63,68,224,233],"multiple":[64],"classification":[65],"recognition":[67],"is":[72,107,181],"created":[73],"in":[74,199,229],"categorize":[77],"3402":[78],"images.":[81],"The":[82,104,139,167,177,204],"model":[83],"evaluation":[84],"metrics":[85],"include":[86],"accuracy,":[87,145],"macro-average":[88,90,92],"precision,":[89,146],"sensitivity,":[91,147],"F1":[93],"value,":[94],"subject":[95,152],"working":[96,153],"curves,":[98],"area":[100,149],"under":[101,150],"curve.":[103],"model's":[105,112,168],"credibility":[106],"evaluated":[108],"by":[109],"testing":[110],"decision":[113,169],"basis":[114,170],"using":[115,134],"gradient-weighted":[117],"class":[118],"activation":[119],"mapping":[120],"method.":[121],"Complete":[122],"developing":[123],"image":[127],"system's":[131],"human-computer":[132],"interface":[133],"PyQt":[136],"toolkit.":[137],"Results:":[138],"Inception":[140],"V3-Xception":[141],"model\u2019":[143],"s":[144],"curve":[155],"test":[158],"set":[159],"were":[160],"0.9673,":[161],"0.9521,":[162],"0.9528,":[163],"0.9988,":[165],"respectively.":[166],"aligned":[171],"with":[172],"ophthalmologists'":[173],"rationale.":[176],"auxiliary":[178],"portable,":[182],"reliable,":[183],"has":[185],"easy-to-use":[187],"interface.":[188],"It":[189],"can":[190,216],"recognize":[191],"intelligently":[195],"analyze":[197],"thermograms":[198],"interpretable":[201],"manner.":[202],"Conclusion:":[203],"learning-based":[206],"aided":[208],"images,":[214,227],"quickly":[217],"accurately":[219],"identify":[220],"six":[221],"distinct":[222],"types":[223],"aids":[228],"evolution":[232],"diagnosis.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}