{"id":"https://openalex.org/W7161113457","doi":"https://doi.org/10.48550/arxiv.2605.13798","title":"VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence","display_name":"VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161113457","doi":"https://doi.org/10.48550/arxiv.2605.13798"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13798","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.2605.13798","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136139941","display_name":"Guney Tombak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tombak, Guney","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075176537","display_name":"Ertun\u00e7 Erdil","orcid":"https://orcid.org/0000-0001-6235-4574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erdil, Ertunc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136164488","display_name":"Ender Konukoglu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Konukoglu, Ender","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/T10036","display_name":"Advanced Neural Network Applications","score":0.31459999084472656,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.31459999084472656,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.302700012922287,"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":0.14169999957084656,"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/voxel","display_name":"Voxel","score":0.6367999911308289},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5302000045776367},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.4643999934196472},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4138000011444092},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.40130001306533813},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4002000093460083},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.39309999346733093}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8166999816894531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7585999965667725},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.694599986076355},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6367999911308289},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4138000011444092},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C2776673561","wikidata":"https://www.wikidata.org/wiki/Q655357","display_name":"Atlas (anatomy)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13798","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.2605.13798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13798","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cross-modal":[0],"3D":[1,204],"medical":[2],"image":[3,55],"analysis":[4,217],"requires":[5],"voxelwise":[6,172],"representations":[7,67,86],"that":[8,23,68,113],"remain":[9],"anatomically":[10],"consistent":[11],"across":[12],"imaging":[13],"contrasts,":[14],"scanners,":[15],"and":[16,45,65,139,164,175,194,202,224],"acquisition":[17],"protocols.":[18],"Recent":[19],"work":[20],"has":[21],"shown":[22],"frozen":[24,88],"2D":[25,89],"Vision":[26],"Transformer":[27],"(ViT)":[28],"foundation":[29,91],"models":[30],"can":[31,149],"support":[32],"such":[33],"representations,":[34],"but":[35],"typical":[36],"pipelines":[37],"extract":[38],"features":[39,48],"along":[40],"a":[41,50,58,78,104,210],"single":[42],"anatomical":[43,121],"axis":[44],"adapt":[46],"those":[47],"inside":[49],"registration":[51,196],"solver":[52],"for":[53,82,190,214],"one":[54],"pair":[56],"at":[57,232],"time,":[59,130],"leaving":[60],"complementary":[61],"viewing":[62],"directions":[63,122],"unused":[64],"producing":[66],"do":[69],"not":[70],"transfer":[71,185],"to":[72,118],"new":[73,131],"volumes.":[74],"We":[75,157],"introduce":[76],"VoxCor,":[77],"training-free":[79],"fit--transform":[80],"method":[81],"reusable":[83,211],"volumetric":[84],"feature":[85,126,212],"from":[87],"ViT":[90,101,137],"models.":[92],"During":[93],"an":[94],"offline":[95],"fitting":[96],"phase,":[97],"VoxCor":[98,159,179,208],"combines":[99],"triplanar":[100,125,136],"inference":[102,138],"with":[103,199],"compact":[105],"closed-form":[106],"weighted":[107],"partial":[108],"least":[109],"squares":[110],"(WPLS)":[111],"projection":[112,141],"uses":[114],"fitting-time":[115],"voxel":[116],"correspondences":[117,148],"select":[119],"modality-stable":[120],"in":[123],"the":[124,181],"space.":[127],"At":[128],"transform":[129],"volumes":[132],"are":[133,227],"mapped":[134],"by":[135,154],"linear":[140],"alone,":[142],"without":[143],"fine-tuning":[144],"or":[145],"registration.":[146,220],"Voxel":[147],"then":[150],"be":[151],"queried":[152],"directly":[153],"nearest-neighbor":[155],"search.":[156],"evaluate":[158],"on":[160,230],"intra-subject":[161],"Abdomen":[162],"MR--CT":[163],"inter-subject":[165],"HCP":[166],"T2w--T1w":[167],"tasks":[168],"using":[169],"deformable":[170],"registration,":[171],"k-nearest-neighbor":[173],"segmentation,":[174],"segmentation-center":[176],"landmark":[177],"localization.":[178],"improves":[180],"hardest":[182],"cross-subject,":[183],"cross-modality":[184],"settings,":[186],"reduces":[187],"encoder":[188],"sensitivity":[189],"dense":[191],"correspondence":[192],"transfer,":[193],"yields":[195],"performance":[197],"competitive":[198],"handcrafted":[200],"descriptors":[201],"learned":[203],"features.":[205],"This":[206],"positions":[207],"as":[209],"layer":[213],"downstream":[215],"multimodal":[216],"beyond":[218],"pairwise":[219],"Code,":[221],"configuration":[222],"files,":[223],"implementation":[225],"details":[226],"publicly":[228],"available":[229],"GitHub":[231],"\\href{https://github.com/guneytombak/VoxCor}{guneytombak/VoxCor}.":[233]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-15T00:00:00"}
