{"id":"https://openalex.org/W7131428276","doi":"https://doi.org/10.48550/arxiv.2602.21179","title":"Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision","display_name":"Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131428276","doi":"https://doi.org/10.48550/arxiv.2602.21179"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.21179","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040501884","display_name":"Nicol\u00e1s Gaggion","orcid":"https://orcid.org/0000-0002-6684-5300"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gaggion, Nicol\u00e1s","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126823499","display_name":"Maria J. Ledesma-Carbayo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ledesma-Carbayo, Maria J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064662363","display_name":"Stergios Christodoulidis","orcid":"https://orcid.org/0000-0002-8773-1070"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christodoulidis, Stergios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126837505","display_name":"Maria Vakalopoulou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vakalopoulou, Maria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5032685263","display_name":"Enzo Ferrante","orcid":"https://orcid.org/0000-0002-8500-788X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferrante, Enzo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":0.3352000117301941,"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":0.3352000117301941,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.1565999984741211,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12759999930858612,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.9269000291824341},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7077999711036682},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.46790000796318054},{"id":"https://openalex.org/keywords/atlas","display_name":"Atlas (anatomy)","score":0.46700000762939453},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4553000032901764},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4198000133037567},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4154999852180481},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.41110000014305115}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9269000291824341},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7077999711036682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6822999715805054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6815999746322632},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C2776673561","wikidata":"https://www.wikidata.org/wiki/Q655357","display_name":"Atlas (anatomy)","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4189999997615814},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4154999852180481},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26339998841285706},{"id":"https://openalex.org/C3004257","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Correspondence problem","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.21179","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.21179","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.21179","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":"pmh:doi:10.48550/arxiv.2602.21179","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":null,"raw_type":"Article"},"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":{"Graph-based":[0],"medical":[1],"image":[2],"segmentation":[3,144,202],"represents":[4],"anatomical":[5,114,151,182],"structures":[6],"using":[7,54],"boundary":[8,186],"graphs,":[9],"providing":[10],"fixed-topology":[11],"landmarks":[12,33],"and":[13,81,88,131,166,212],"inherent":[14],"population-level":[15],"correspondences.":[16,215],"However,":[17],"their":[18],"clinical":[19],"adoption":[20],"has":[21],"been":[22],"hindered":[23],"by":[24,76,184],"a":[25,47,189],"major":[26],"requirement:":[27],"training":[28],"datasets":[29],"with":[30,72,112],"manually":[31],"annotated":[32],"that":[34,49,105,170,208],"maintain":[35,209],"point-to-point":[36],"correspondences":[37,141],"across":[38,116,159],"patients":[39,117],"rarely":[40],"exist":[41],"in":[42],"practice.":[43],"We":[44],"introduce":[45],"Mask-HybridGNet,":[46],"framework":[48,103,195],"trains":[50],"graph-based":[51],"models":[52,207],"directly":[53],"standard":[55,201],"pixel-wise":[56],"masks,":[57,145],"eliminating":[58],"the":[59,197],"need":[60],"for":[61],"manual":[62],"landmark":[63,74,90,107],"annotations.":[64],"Our":[65],"approach":[66],"aligns":[67],"variable-length":[68],"ground":[69],"truth":[70],"boundaries":[71],"fixed-length":[73],"predictions":[75],"combining":[77],"Chamfer":[78],"distance":[79],"supervision":[80],"edge-based":[82],"regularization":[83],"to":[84,148,204],"ensure":[85],"local":[86],"smoothness":[87],"regular":[89],"distribution,":[91],"further":[92],"refined":[93],"via":[94],"differentiable":[95],"rasterization.":[96],"A":[97],"significant":[98],"emergent":[99],"property":[100],"of":[101,200],"this":[102],"is":[104],"predicted":[106],"positions":[108],"become":[109],"consistently":[110],"associated":[111],"specific":[113],"locations":[115],"without":[118],"explicit":[119],"correspondence":[120],"supervision.":[121],"This":[122,194],"implicit":[123,214],"atlas":[124],"learning":[125],"enables":[126],"temporal":[127],"tracking,":[128],"cross-slice":[129],"reconstruction,":[130],"morphological":[132],"population":[133],"analyses.":[134],"Beyond":[135],"direct":[136],"segmentation,":[137],"Mask-HybridGNet":[138],"can":[139],"extract":[140],"from":[142,153],"existing":[143],"allowing":[146],"it":[147],"generate":[149],"stable":[150],"atlases":[152],"any":[154],"high-quality":[155],"pixel-based":[156,178],"model.":[157],"Experiments":[158],"chest":[160],"radiography,":[161],"cardiac":[162,164],"ultrasound,":[163],"MRI,":[165],"fetal":[167],"imaging":[168],"demonstrate":[169],"our":[171],"model":[172],"achieves":[173],"competitive":[174],"results":[175],"against":[176],"state-of-the-art":[177],"methods,":[179],"while":[180],"ensuring":[181],"plausibility":[183],"enforcing":[185],"connectivity":[187],"through":[188],"fixed":[190],"graph":[191],"adjacency":[192],"matrix.":[193],"leverages":[196],"vast":[198],"availability":[199],"masks":[203],"build":[205],"structured":[206],"topological":[210],"integrity":[211],"provide":[213]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-26T00:00:00"}