{"id":"https://openalex.org/W4403577310","doi":"https://doi.org/10.48550/arxiv.2410.12053","title":"SOE: SO(3)-Equivariant 3D MRI Encoding","display_name":"SOE: SO(3)-Equivariant 3D MRI Encoding","publication_year":2024,"publication_date":"2024-10-15","ids":{"openalex":"https://openalex.org/W4403577310","doi":"https://doi.org/10.48550/arxiv.2410.12053"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2410.12053","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.12053","pdf_url":"https://arxiv.org/pdf/2410.12053","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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/2410.12053","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056351004","display_name":"S. L. He","orcid":"https://orcid.org/0009-0008-9337-3055"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"He, Shizhe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048218427","display_name":"Magdalini Paschali","orcid":"https://orcid.org/0000-0003-4967-3137"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paschali, Magdalini","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","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":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082479164","display_name":"Adnan Masood","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masood, Adnan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030301636","display_name":"Akshay Chaudhari","orcid":"https://orcid.org/0000-0001-9350-4280"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chaudhari, Akshay","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","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":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5056351004"],"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9276000261306763,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9276000261306763,"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"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9085000157356262,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9057999849319458,"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/equivariant-map","display_name":"Equivariant map","score":0.7955859899520874},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6185590028762817},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34677934646606445},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2835019826889038},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2468678057193756},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.20855364203453064}],"concepts":[{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.7955859899520874},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6185590028762817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34677934646606445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2835019826889038},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2468678057193756},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.20855364203453064}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2410.12053","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.12053","pdf_url":"https://arxiv.org/pdf/2410.12053","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2410.12053","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2410.12053","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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:2410.12053","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.12053","pdf_url":"https://arxiv.org/pdf/2410.12053","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4403577310.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3148895720","https://openalex.org/W2393913406","https://openalex.org/W2356579025","https://openalex.org/W2736697936","https://openalex.org/W3211835374","https://openalex.org/W30258475","https://openalex.org/W2548611373"],"abstract_inverted_index":{"Representation":[0],"learning":[1,13,157],"has":[2],"become":[3],"increasingly":[4],"important,":[5],"especially":[6],"as":[7,54,159],"powerful":[8],"models":[9,40],"have":[10],"shifted":[11],"towards":[12],"latent":[14],"representations":[15],"before":[16],"fine-tuning":[17],"for":[18,42,100,168],"downstream":[19,222],"tasks.":[20],"This":[21,150],"approach":[22,151,244],"is":[23,44,142,251,264],"particularly":[24],"valuable":[25],"in":[26,112,115,126,145,176],"leveraging":[27],"the":[28,76,127,138,146,169,172,184,197,210,221,236],"structural":[29,199,211],"information":[30,88,202],"within":[31,89],"brain":[32,90,233],"anatomy.":[33],"However,":[34],"a":[35,93,97,162],"common":[36],"limitation":[37],"of":[38,171,186,213,224,235,257],"recent":[39],"developed":[41],"MRIs":[43,103,212],"their":[45],"tendency":[46],"to":[47,63,83,109,137,220],"ignore":[48],"or":[49],"remove":[50],"geometric":[51,64,73,124],"information,":[52],"such":[53],"translation":[55],"and":[56,200,227],"rotation,":[57],"thereby":[58],"creating":[59],"invariance":[60],"with":[61,107,218],"respect":[62,108,219],"operations.":[65],"We":[66,205,240],"contend":[67],"that":[68,104,132,166,177,242],"incorporating":[69],"knowledge":[70],"about":[71],"these":[72],"transformations":[74],"into":[75],"model":[77],"can":[78],"significantly":[79],"enhance":[80],"its":[81],"ability":[82],"learn":[84],"more":[85,203],"detailed":[86],"anatomical":[87,201],"structures.":[91],"As":[92],"result,":[94],"we":[95,130,160,182],"propose":[96],"novel":[98],"method":[99,195],"encoding":[101],"3D":[102,113],"enforces":[105],"equivariance":[106,125],"all":[110],"rotations":[111],"space,":[114,129],"other":[116,248],"words,":[117],"SO(3)-equivariance":[118],"(SOE).":[119],"By":[120],"explicitly":[121],"modeling":[122],"this":[123],"representation":[128,148,156,163,190],"ensure":[131],"any":[133],"rotational":[134],"operation":[135,175],"applied":[136],"input":[139],"image":[140],"space":[141,165,191],"also":[143,252],"reflected":[144],"embedding":[147],"space.":[149,178],"requires":[152],"moving":[153],"beyond":[154],"traditional":[155],"methods,":[158],"need":[161],"vector":[164,187],"allows":[167],"application":[170],"same":[173],"SO(3)":[174],"To":[179],"facilitate":[180],"this,":[181],"leverage":[183],"concept":[185],"neurons.":[188],"The":[189,262],"formed":[192],"by":[193],"our":[194,243],"captures":[196],"brain's":[198],"effectively.":[204],"evaluate":[206],"SOE":[207],"pretrained":[208],"on":[209],"two":[214],"public":[215],"data":[216,238],"sets":[217],"task":[223],"predicting":[225],"age":[226],"diagnosing":[228],"Alzheimer's":[229],"Disease":[230],"from":[231],"T1-weighted":[232],"scans":[234],"ADNI":[237],"set.":[239],"demonstrate":[241],"not":[245],"only":[246],"outperforms":[247],"methods":[249],"but":[250],"robust":[253],"against":[254],"various":[255],"degrees":[256],"rotation":[258],"along":[259],"different":[260],"axes.":[261],"code":[263],"available":[265],"at":[266],"https://github.com/shizhehe/SOE-representation-learning.":[267]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}