{"id":"https://openalex.org/W4387323320","doi":"https://doi.org/10.48550/arxiv.2310.00213","title":"LSOR: Longitudinally-Consistent Self-Organized Representation Learning","display_name":"LSOR: Longitudinally-Consistent Self-Organized Representation Learning","publication_year":2023,"publication_date":"2023-09-30","ids":{"openalex":"https://openalex.org/W4387323320","doi":"https://doi.org/10.48550/arxiv.2310.00213"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2310.00213","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.00213","pdf_url":"https://arxiv.org/pdf/2310.00213","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2310.00213","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082029341","display_name":"Jiahong Ouyang","orcid":"https://orcid.org/0000-0002-0434-5757"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ouyang, Jiahong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101563032","display_name":"Qingyu Zhao","orcid":"https://orcid.org/0000-0002-9694-6022"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Qingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015355317","display_name":"Ehsan Adeli","orcid":"https://orcid.org/0000-0002-0579-7763"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adeli, Ehsan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019479187","display_name":"Wei Peng","orcid":"https://orcid.org/0000-0002-2892-5764"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065577825","display_name":"Greg Zaharchuk","orcid":"https://orcid.org/0000-0001-5781-8848"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaharchuk, Greg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055107125","display_name":"Kilian M. Pohl","orcid":"https://orcid.org/0000-0001-5416-5159"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pohl, Kilian M.","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":true,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9847000241279602,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9847000241279602,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11184","display_name":"Neonatal and fetal brain pathology","score":0.9532999992370605,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/interpretability","display_name":"Interpretability","score":0.8405275344848633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5925187468528748},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5826844573020935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.577728271484375},{"id":"https://openalex.org/keywords/self-organizing-map","display_name":"Self-organizing map","score":0.5624858736991882},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5402241945266724},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.48686474561691284},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47723034024238586},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4578371047973633},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.422657310962677},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39700430631637573},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.2712594270706177},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.08661431074142456}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8405275344848633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5925187468528748},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5826844573020935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.577728271484375},{"id":"https://openalex.org/C111168008","wikidata":"https://www.wikidata.org/wiki/Q1136838","display_name":"Self-organizing map","level":3,"score":0.5624858736991882},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5402241945266724},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.48686474561691284},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47723034024238586},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4578371047973633},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.422657310962677},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39700430631637573},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2712594270706177},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.08661431074142456},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2310.00213","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.00213","pdf_url":"https://arxiv.org/pdf/2310.00213","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2310.00213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.00213","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2310.00213","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.00213","pdf_url":"https://arxiv.org/pdf/2310.00213","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.5,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1372586559","display_name":null,"funder_award_id":"22-KUJoint-02","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320326308","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387323320.pdf","grobid_xml":"https://content.openalex.org/works/W4387323320.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4361193272","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"Interpretability":[0],"is":[1,20,134,235],"a":[2,49,64,74,106,157],"key":[3],"issue":[4,19],"when":[5],"applying":[6],"deep":[7,29],"learning":[8,30,61,130],"models":[9],"to":[10,16,48,69,162,172,184,213],"longitudinal":[11,117,170,185],"brain":[12,91,112,118,163],"MRIs.":[13],"One":[14],"way":[15],"address":[17],"this":[18],"by":[21,28,111,150,165],"visualizing":[22],"the":[23,37,45,55,78,99,132,145,173,178,188,208,214],"high-dimensional":[24,56,65],"latent":[25,38,66,158,199],"spaces":[26],"generated":[27],"via":[31],"self-organizing":[32],"maps":[33,44],"(SOM).":[34],"SOM":[35,62,80,102,180],"separates":[36],"space":[39,67,159,200],"into":[40],"clusters":[41],"and":[42,201,225],"then":[43],"cluster":[46,147],"centers":[47],"discrete":[50],"(typically":[51],"2D)":[52],"grid":[53,81],"preserving":[54],"relationship":[57],"between":[58],"clusters.":[59],"However,":[60],"in":[63,73],"tends":[68],"be":[70],"unstable,":[71],"especially":[72],"self-supervision":[75],"setting.":[76],"Furthermore,":[77,153],"learned":[79],"does":[82],"not":[83],"necessarily":[84],"capture":[85],"clinically":[86],"interesting":[87],"information,":[88],"such":[89],"as":[90,138],"age.":[92],"To":[93],"resolve":[94],"these":[95],"issues,":[96],"we":[97],"propose":[98],"first":[100],"self-supervised":[101],"approach":[103,155],"that":[104],"derives":[105],"high-dimensional,":[107],"interpretable":[108,198],"representation":[109],"stratified":[110,160],"age":[113,164],"solely":[114],"based":[115],"on":[116,141],"MRIs":[119,171,186],"(i.e.,":[120],"without":[121],"demographic":[122],"or":[123,204],"cognitive":[124,223],"information).":[125],"Called":[126],"Longitudinally-consistent":[127],"Self-Organized":[128],"Representation":[129],"(LSOR),":[131],"method":[133],"stable":[135],"during":[136],"training":[137],"it":[139],"relies":[140],"soft":[142],"clustering":[143],"(vs.":[144],"hard":[146],"assignments":[148],"used":[149],"existing":[151],"SOM).":[152],"our":[154],"generates":[156,196],"according":[161],"aligning":[166],"trajectories":[167],"inferred":[168],"from":[169],"reference":[174],"vector":[175],"associated":[176],"with":[177,211],"corresponding":[179],"cluster.":[181],"When":[182],"applied":[183],"of":[187,217,230],"Alzheimer's":[189],"Disease":[190],"Neuroimaging":[191],"Initiative":[192],"(ADNI,":[193],"N=632),":[194],"LSOR":[195],"an":[197],"achieves":[202],"comparable":[203],"higher":[205],"accuracy":[206],"than":[207],"state-of-the-art":[209],"representations":[210],"respect":[212],"downstream":[215],"tasks":[216],"classification":[218],"(static":[219],"vs.":[220],"progressive":[221],"mild":[222],"impairment)":[224],"regression":[226],"(determining":[227],"ADAS-Cog":[228],"score":[229],"all":[231],"subjects).":[232],"The":[233],"code":[234],"available":[236],"at":[237],"https://github.com/ouyangjiahong/longitudinal-som-single-modality.":[238]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
