{"id":"https://openalex.org/W7140421436","doi":"https://doi.org/10.48550/arxiv.2603.23694","title":"CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration","display_name":"CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140421436","doi":"https://doi.org/10.48550/arxiv.2603.23694"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23694","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23694","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.23694","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130669960","display_name":"Eytan Kats","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kats, Eytan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130658349","display_name":"Christoph Grossbroehmer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grossbroehmer, Christoph","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093843256","display_name":"Ziad Al-Haj Hemidi","orcid":"https://orcid.org/0000-0002-9823-802X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hemidi, Ziad Al-Haj","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017406982","display_name":"Fenja Falta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Falta, Fenja","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107762614","display_name":"Wiebke Heyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heyer, Wiebke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130630303","display_name":"Mattias P. Heinrich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heinrich, Mattias P.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5130669960"],"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.7976999878883362,"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":0.7976999878883362,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.062199998646974564,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.01899999938905239,"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/image-registration","display_name":"Image registration","score":0.7206000089645386},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5893999934196472},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4553000032901764},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4498000144958496},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.40709999203681946},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.3903999924659729},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.37059998512268066}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7789999842643738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336999773979187},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.7206000089645386},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5893999934196472},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.567300021648407},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37059998512268066},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23694","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23694","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"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.23694","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23694","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0],"image":[1,9,131],"registration":[2,36,77,104,121,132,152],"is":[3],"a":[4,66],"fundamental":[5],"task":[6],"in":[7],"medical":[8],"analysis,":[10],"enabling":[11],"the":[12,33,59,76,82,101,109,120,144,151],"alignment":[13],"of":[14,35,84,146],"images":[15],"from":[16],"different":[17],"modalities":[18],"or":[19],"time":[20],"points.":[21],"However,":[22],"intensity":[23],"inconsistencies":[24],"and":[25,103,129,137],"nonlinear":[26],"tissue":[27,96],"deformations":[28],"pose":[29],"significant":[30],"challenges":[31],"to":[32,50,87,95],"robustness":[34],"methods.":[37,160],"Recent":[38],"approaches":[39],"leveraging":[40],"self-supervised":[41],"representation":[42],"learning":[43,73,86,148],"show":[44],"promise":[45],"by":[46],"pre-training":[47],"feature":[48,90],"extractors":[49],"generate":[51],"robust":[52,89],"anatomical":[53],"embeddings,":[54],"that":[55,69,92,108,143],"farther":[56],"used":[57],"for":[58,119],"registration.":[60],"In":[61],"this":[62],"work,":[63],"we":[64,106],"propose":[65],"novel":[67],"framework":[68,153],"integrates":[70],"equivariant":[71],"contrastive":[72,85,102,147],"directly":[74,149],"into":[75,150],"model.":[78],"Our":[79],"approach":[80],"leverages":[81],"power":[83],"learn":[88],"representations":[91,111],"are":[93,112],"invariant":[94],"deformations.":[97],"By":[98],"jointly":[99],"optimizing":[100],"objectives,":[105],"ensure":[107],"learned":[110],"not":[113],"only":[114],"informative":[115],"but":[116],"also":[117],"suitable":[118],"task.":[122],"We":[123],"evaluate":[124],"our":[125],"method":[126],"on":[127],"abdominal":[128],"thoracic":[130],"tasks,":[133],"including":[134],"both":[135],"intra-patient":[136],"inter-patient":[138],"scenarios.":[139],"Experimental":[140],"results":[141],"demonstrate":[142],"integration":[145],"significantly":[154],"improves":[155],"performance,":[156],"surpassing":[157],"strong":[158],"baseline":[159]},"counts_by_year":[],"updated_date":"2026-03-27T06:05:27.210665","created_date":"2026-03-27T00:00:00"}