{"id":"https://openalex.org/W7156070792","doi":"https://doi.org/10.48550/arxiv.2604.22139","title":"Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography","display_name":"Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7156070792","doi":"https://doi.org/10.48550/arxiv.2604.22139"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22139","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22139","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.22139","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113191858","display_name":"Tania Haghighi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haghighi, Tania","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134675273","display_name":"Sina Gholami","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gholami, Sina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134674591","display_name":"Hamed Tabkhi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tabkhi, Hamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072253597","display_name":"Minhaj Nur Alam","orcid":"https://orcid.org/0000-0003-3095-2232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alam, Minhaj Nur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9291999936103821,"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":0.9291999936103821,"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/T12484","display_name":"Retinopathy of Prematurity Studies","score":0.007000000216066837,"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/T11569","display_name":"Optical Coherence Tomography Applications","score":0.006399999838322401,"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/optical-coherence-tomography","display_name":"Optical coherence tomography","score":0.7404999732971191},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5992000102996826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5877000093460083},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.48730000853538513},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.448199987411499},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4359000027179718},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.4311999976634979},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42989999055862427},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.428600013256073}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7638999819755554},{"id":"https://openalex.org/C2778818243","wikidata":"https://www.wikidata.org/wiki/Q899552","display_name":"Optical coherence tomography","level":2,"score":0.7404999732971191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372000217437744},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5992000102996826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5877000093460083},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4359000027179718},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36649999022483826},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.34220001101493835},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22139","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22139","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.22139","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22139","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5983338356018066}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"automated":[1],"analysis":[2],"of":[3,47,62],"Optical":[4],"Coherence":[5],"Tomography":[6],"(OCT)":[7],"imaging":[8,37,117],"is":[9],"crucial":[10],"for":[11,22],"diagnosing":[12],"retinal":[13,64,99],"disorders":[14],"but":[15],"faces":[16],"a":[17,80],"critical":[18],"barrier:":[19],"the":[20,59,140,171],"need":[21],"expensive,":[23],"labor-intensive":[24],"expert":[25],"annotations.":[26],"Supervised":[27],"deep":[28],"learning":[29,105],"models":[30],"struggle":[31],"to":[32,43,88,106],"generalize":[33],"across":[34,115,188],"diverse":[35],"pathologies,":[36],"devices,":[38],"and":[39,102,124,132,152,182],"patient":[40],"populations":[41],"due":[42],"their":[44],"restricted":[45],"vocabulary":[46],"annotated":[48],"abnormalities.":[49],"We":[50],"propose":[51],"an":[52],"unsupervised":[53,175],"anomaly":[54,176],"detection":[55],"framework":[56],"that":[57],"learns":[58],"normative":[60],"distribution":[61],"healthy":[63,108],"anatomy":[65],"without":[66,135],"lesion":[67],"annotations,":[68],"directly":[69],"addressing":[70],"annotation":[71],"efficiency":[72],"challenges":[73],"in":[74],"clinical":[75,95],"deployment.":[76],"Our":[77],"approach":[78],"leverages":[79],"discrete":[81],"latent":[82],"model":[83,113],"trained":[84],"on":[85,158],"normal":[86],"B-scans":[87],"capture":[89],"OCT-specific":[90],"structural":[91],"patterns.":[92],"To":[93],"enhance":[94],"robustness,":[96],"we":[97],"incorporate":[98],"layer-aware":[100],"supervision":[101],"structured":[103],"triplet":[104],"separate":[107],"from":[109],"pathological":[110],"representations,":[111],"improving":[112],"reliability":[114],"varied":[116],"conditions.":[118],"During":[119],"inference,":[120],"anomalies":[121],"are":[122],"detected":[123],"localized":[125],"via":[126],"reconstruction":[127],"discrepancies,":[128],"enabling":[129],"both":[130],"image":[131],"pixel-level":[133],"identification":[134],"requiring":[136],"disease-specific":[137],"labels.":[138],"On":[139,170],"Kermany":[141],"dataset":[142],"(AUROC:":[143],"0.799),":[144],"our":[145],"method":[146],"substantially":[147],"outperforms":[148],"VAE,":[149],"VQVAE,":[150],"VQGAN,":[151],"f-AnoGAN":[153],"baselines.":[154],"Critically,":[155],"cross-dataset":[156],"evaluation":[157],"Srinivasan":[159],"achieves":[160,178],"AUROC":[161],"0.884":[162],"with":[163],"superior":[164],"generalization,":[165],"demonstrating":[166],"robust":[167],"domain":[168],"adaptation.":[169],"external":[172],"RETOUCH":[173],"benchmark,":[174],"segmentation":[177],"competitive":[179],"Dice":[180],"(0.200)":[181],"mIoU":[183],"(0.117)":[184],"scores,":[185],"validating":[186],"reproducibility":[187],"institutions.":[189]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-28T00:00:00"}
