{"id":"https://openalex.org/W7170143686","doi":"https://doi.org/10.48550/arxiv.2607.19696","title":"PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis","display_name":"PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis","publication_year":2026,"publication_date":"2026-07-22","ids":{"openalex":"https://openalex.org/W7170143686","doi":"https://doi.org/10.48550/arxiv.2607.19696"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.19696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.19696","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.2607.19696","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011224218","display_name":"Duong Vu","orcid":"https://orcid.org/0000-0002-4795-2522"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vu, Duong Ngoc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073166196","display_name":"H S. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Hai Son","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034859176","display_name":"Trong Nghia-Nguyen","orcid":"https://orcid.org/0000-0001-9954-8452"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Trong-Nghia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143471722","display_name":"Bien Tran Van","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Van, Bien Tran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090497165","display_name":"Trang Xuan","orcid":"https://orcid.org/0009-0008-5016-1810"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuan, Trang Mai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126472503","display_name":"Huan Vu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vu, Huan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137769474","display_name":"Thien Van Luong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Van Luong, Thien","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/T14510","display_name":"Medical Imaging and Analysis","score":0.9962999820709229,"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":0.9962999820709229,"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"}},{"id":"https://openalex.org/T10238","display_name":"Spine and Intervertebral Disc Pathology","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2734","display_name":"Pathology and Forensic Medicine"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.0003000000142492354,"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/benchmark","display_name":"Benchmark (surveying)","score":0.666100025177002},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6132000088691711},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.5450999736785889},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4172999858856201},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39890000224113464},{"id":"https://openalex.org/keywords/sagittal-plane","display_name":"Sagittal plane","score":0.39800000190734863},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.38530001044273376}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.666100025177002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6401000022888184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6255000233650208},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6132000088691711},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C178910020","wikidata":"https://www.wikidata.org/wiki/Q2211994","display_name":"Sagittal plane","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.33959999680519104},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3100999891757965},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C205383261","wikidata":"https://www.wikidata.org/wiki/Q7392570","display_name":"SPINE (molecular biology)","level":2,"score":0.26460000872612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.19696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.19696","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.2607.19696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.19696","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"accurate":[1],"diagnosis":[2],"of":[3,19,69,97],"spinal":[4],"pathologies":[5],"depends":[6],"heavily":[7],"on":[8],"radiological":[9],"interpretation,":[10],"yet":[11],"automated":[12],"systems":[13],"are":[14,121],"hindered":[15],"by":[16,124],"the":[17,66,82,87,114],"lack":[18],"diverse,":[20],"high-quality":[21],"benchmarks.":[22],"In":[23],"this":[24,110],"study,":[25],"we":[26],"present":[27],"PhenSPINE,":[28],"a":[29,49,59,93,134],"Magnetic":[30],"Resonance":[31],"Imaging":[32],"dataset":[33,120],"comprising":[34],"16,813":[35],"images":[36,115],"from":[37,127],"250":[38],"patients,":[39],"curated":[40],"to":[41,63,109],"facilitate":[42],"advanced":[43],"deep":[44],"learning":[45],"research.":[46],"We":[47,99],"propose":[48],"robust":[50,89,135],"diagnostic":[51,90],"benchmark":[52],"that":[53,81,101],"integrates":[54],"state-of-theart":[55],"convolutional":[56],"backbones":[57],"with":[58],"Positional":[60],"Encoding":[61],"mechanism":[62],"explicitly":[64],"model":[65],"anatomical":[67,129],"context":[68],"intervertebral":[70],"discs.":[71],"Evaluating":[72],"across":[73,116],"four":[74],"standard":[75],"MRI":[76],"sequences,":[77],"our":[78,119],"experiments":[79],"demonstrate":[80],"Sagittal":[83],"T2-weighted":[84],"sequence":[85,142],"offers":[86,138],"most":[88],"value,":[91],"achieving":[92],"superior":[94],"Macro":[95],"F1-score":[96],"50.31%.":[98],"find":[100],"multisequence":[102],"fusion":[103],"strategies":[104],"yield":[105],"inferior":[106],"performance":[107],"compared":[108],"single-sequence":[111],"baseline,":[112],"as":[113],"sequences":[117],"in":[118],"significantly":[122],"compromised":[123],"noise":[125],"interference":[126],"surrounding":[128],"regions.":[130],"This":[131],"work":[132],"establishes":[133],"baseline":[136],"and":[137],"critical":[139],"insights":[140],"into":[141],"selection":[143],"for":[144],"spine":[145],"analysis.":[146]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-24T00:00:00"}
