{"id":"https://openalex.org/W2594048487","doi":"https://doi.org/10.1117/12.2254286","title":"Learning deep similarity in fundus photography","display_name":"Learning deep similarity in fundus photography","publication_year":2017,"publication_date":"2017-02-24","ids":{"openalex":"https://openalex.org/W2594048487","doi":"https://doi.org/10.1117/12.2254286","mag":"2594048487"},"language":"en","primary_location":{"id":"doi:10.1117/12.2254286","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2254286","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5032637108","display_name":"Piotr Chudzik","orcid":null},"institutions":[{"id":"https://openalex.org/I51532219","display_name":"University of Lincoln","ror":"https://ror.org/03yeq9x20","country_code":"GB","type":"education","lineage":["https://openalex.org/I51532219"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Piotr Chudzik","raw_affiliation_strings":["Univ. of Lincoln (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Univ. of Lincoln (United Kingdom)","institution_ids":["https://openalex.org/I51532219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019263484","display_name":"Bashir Al-Diri","orcid":"https://orcid.org/0000-0002-9526-9058"},"institutions":[{"id":"https://openalex.org/I51532219","display_name":"University of Lincoln","ror":"https://ror.org/03yeq9x20","country_code":"GB","type":"education","lineage":["https://openalex.org/I51532219"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Bashir Al-Diri","raw_affiliation_strings":["Univ. of Lincoln (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Univ. of Lincoln (United Kingdom)","institution_ids":["https://openalex.org/I51532219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014387832","display_name":"Francesco Caliv\u00e1","orcid":"https://orcid.org/0000-0002-0425-7511"},"institutions":[{"id":"https://openalex.org/I51532219","display_name":"University of Lincoln","ror":"https://ror.org/03yeq9x20","country_code":"GB","type":"education","lineage":["https://openalex.org/I51532219"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Francesco Caliva","raw_affiliation_strings":["Univ. of Lincoln (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Univ. of Lincoln (United Kingdom)","institution_ids":["https://openalex.org/I51532219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031251186","display_name":"Giovanni Ometto","orcid":"https://orcid.org/0000-0002-0900-4847"},"institutions":[{"id":"https://openalex.org/I2802335433","display_name":"Aarhus University Hospital","ror":"https://ror.org/040r8fr65","country_code":"DK","type":"healthcare","lineage":["https://openalex.org/I2802335433"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Giovanni Ometto","raw_affiliation_strings":["Aarhus Univ. Hospital (Denmark)"],"affiliations":[{"raw_affiliation_string":"Aarhus Univ. Hospital (Denmark)","institution_ids":["https://openalex.org/I2802335433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042449261","display_name":"Andrew Hunter","orcid":"https://orcid.org/0000-0003-3786-4008"},"institutions":[{"id":"https://openalex.org/I51532219","display_name":"University of Lincoln","ror":"https://ror.org/03yeq9x20","country_code":"GB","type":"education","lineage":["https://openalex.org/I51532219"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Andrew Hunter","raw_affiliation_strings":["Univ. of Lincoln (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Univ. of Lincoln (United Kingdom)","institution_ids":["https://openalex.org/I51532219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032637108"],"corresponding_institution_ids":["https://openalex.org/I51532219"],"apc_list":null,"apc_paid":null,"fwci":0.4166,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63180421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"10133","issue":null,"first_page":"101332A","last_page":"101332A"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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/T10170","display_name":"Retinal Diseases and Treatments","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9693999886512756,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/similarity-learning","display_name":"Similarity learning","score":0.7909030914306641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7799601554870605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.759190559387207},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7210798263549805},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6612879037857056},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6506677865982056},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6367489099502563},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.6198151707649231},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5961337685585022},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5611624121665955},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4736798107624054},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46538203954696655},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4431527853012085},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.42747071385383606},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37593287229537964},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36752164363861084}],"concepts":[{"id":"https://openalex.org/C2779597229","wikidata":"https://www.wikidata.org/wiki/Q17146505","display_name":"Similarity learning","level":3,"score":0.7909030914306641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7799601554870605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759190559387207},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7210798263549805},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6612879037857056},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6506677865982056},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6367489099502563},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.6198151707649231},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5961337685585022},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5611624121665955},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4736798107624054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46538203954696655},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4431527853012085},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.42747071385383606},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37593287229537964},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36752164363861084},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1117/12.2254286","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2254286","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/7fa90ef3-dbe0-4770-85e5-53f2f9a8c213","is_oa":false,"landing_page_url":"https://pure.au.dk/portal/en/publications/7fa90ef3-dbe0-4770-85e5-53f2f9a8c213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chudzik, P, Al-Diri, B, Caliva, F, Ometto, G & Hunter, A 2017, Learning Deep Similarity in Fundus Photography. in Medical Imaging 2017: : Image Processing. SPIE - International Society for Optical Engineering, Proceedings of SPIE , vol. 10133, Conference on Medical Imaging - Image Processing, Orlando, 12/02/2017. https://doi.org/10.1117/12.2254286","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1677409904","https://openalex.org/W1686810756","https://openalex.org/W1806891645","https://openalex.org/W1842610785","https://openalex.org/W1929856797","https://openalex.org/W1955055330","https://openalex.org/W2033178790","https://openalex.org/W2053186076","https://openalex.org/W2095705004","https://openalex.org/W2128237624","https://openalex.org/W2144041313","https://openalex.org/W2146502635","https://openalex.org/W2150769593","https://openalex.org/W2151103935","https://openalex.org/W2157364932","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2384495648","https://openalex.org/W2963446712","https://openalex.org/W6637373629","https://openalex.org/W6637400245","https://openalex.org/W6638444622","https://openalex.org/W6638520917","https://openalex.org/W6638650905","https://openalex.org/W6640083356","https://openalex.org/W6641059627","https://openalex.org/W6658931849","https://openalex.org/W6674330103","https://openalex.org/W6679057852","https://openalex.org/W6681212791","https://openalex.org/W6681435938","https://openalex.org/W6683390034","https://openalex.org/W6684191040","https://openalex.org/W6696761078","https://openalex.org/W6725739302"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2109005765","https://openalex.org/W4315588666","https://openalex.org/W2145721853","https://openalex.org/W3108274592","https://openalex.org/W2906898977","https://openalex.org/W4378194747","https://openalex.org/W2727229559","https://openalex.org/W2798991696","https://openalex.org/W2951292523"],"abstract_inverted_index":{"Similarity":[0],"learning":[1,136],"is":[2,60,187,219],"one":[3],"of":[4,24,39,53,67,70,117,215],"the":[5,19,64,115,156,166,205,213,220],"most":[6],"fundamental":[7],"tasks":[8],"in":[9,18,42,80,90,229],"image":[10,26,106,112],"analysis.":[11],"The":[12,36,51],"ability":[13],"to":[14,63,100,137,223],"extract":[15],"similar":[16],"images":[17,59],"medical":[20,58],"domain":[21],"as":[22,194],"part":[23],"content-based":[25],"retrieval":[27],"(CBIR)":[28],"systems":[29,44],"has":[30],"been":[31],"researched":[32],"for":[33,57,105,134],"many":[34],"years.":[35],"vast":[37],"majority":[38],"methods":[40],"used":[41,123],"CBIR":[43],"are":[45,199],"based":[46],"on":[47],"hand-crafted":[48],"feature":[49,120,169],"descriptors.":[50,121],"approximation":[52],"a":[54,77,91,102,124,130,176,225],"similarity":[55,103,135,157,227],"mapping":[56,228],"difficult":[61],"due":[62],"big":[65],"variety":[66],"pixel-level":[68],"structures":[69],"interest.":[71],"In":[72,94],"fundus":[73],"photography":[74],"(FP)":[75],"analysis,":[76],"subtle":[78],"difference":[79],"e.g.":[81],"lesions":[82],"and":[83,86,144,163,173,197],"vessels":[84,195],"shape":[85],"size":[87],"can":[88,154],"result":[89],"different":[92],"diagnosis.":[93],"this":[95,139,218],"work,":[96],"we":[97,142,181],"demonstrated":[98],"how":[99,207],"learn":[101],"function":[104],"patches":[107,160],"derived":[108],"directly":[109],"from":[110],"FP":[111,159],"data":[113],"without":[114],"need":[116],"manually":[118],"designed":[119],"We":[122,149],"convolutional":[125],"neural":[126],"network":[127],"(CNN)":[128],"with":[129],"novel":[131],"architecture":[132],"adapted":[133],"accomplish":[138],"task.":[140],"Furthermore,":[141],"explored":[143],"studied":[145],"multiple":[146],"CNN":[147],"architectures.":[148],"show":[150],"that":[151,183,191],"our":[152,184],"method":[153],"approximate":[155,224],"between":[158],"more":[161],"efficiently":[162],"accurately":[164],"than":[165],"state-of-":[167],"the-art":[168],"descriptors,":[170],"including":[171],"SIFT":[172],"SURF":[174],"using":[175],"publicly":[177],"available":[178],"dataset.":[179],"Finally,":[180],"observe":[182],"approach,":[185],"which":[186,203],"purely":[188],"data-driven,":[189],"learns":[190],"features":[192],"such":[193],"calibre":[196],"orientation":[198],"important":[200],"discriminative":[201],"factors,":[202],"resembles":[204],"way":[206],"humans":[208],"reason":[209],"about":[210],"similarity.":[211],"To":[212],"best":[214],"authors":[216],"knowledge,":[217],"first":[221],"attempt":[222],"visual":[226],"FP.":[230]},"counts_by_year":[{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
