{"id":"https://openalex.org/W7148490756","doi":"https://doi.org/10.48550/arxiv.2604.00397","title":"Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions","display_name":"Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148490756","doi":"https://doi.org/10.48550/arxiv.2604.00397"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00397","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":null,"license_id":null,"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.2604.00397","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132811798","display_name":"Yuchen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yang, Yuchen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhong, Shuangyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Shuangyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101548867","display_name":"Haijun Yu","orcid":"https://orcid.org/0000-0003-1596-7962"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Haijun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132806262","display_name":"Langcuomu Suo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suo, Langcuomu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Han, Hongbin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Hongbin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132805893","display_name":"Florian Putz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Putz, Florian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132813200","display_name":"Yixing Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yixing","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5132811798"],"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/T11600","display_name":"Brain Metastases and Treatment","score":0.4871000051498413,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11600","display_name":"Brain Metastases and Treatment","score":0.4871000051498413,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10750000178813934,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.06780000030994415,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.71670001745224},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6068000197410583},{"id":"https://openalex.org/keywords/percentile","display_name":"Percentile","score":0.5814999938011169},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.578000009059906},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.5759000182151794},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5347999930381775},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.506600022315979},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4627000093460083}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8021000027656555},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.71670001745224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6068000197410583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5860000252723694},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.5814999938011169},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.578000009059906},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.5759000182151794},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5347999930381775},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4627000093460083},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3601999878883362},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3352000117301941},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3111000061035156},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00397","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00397","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":null,"license_id":null,"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":{"Background:":[0],"Deep":[1],"learning":[2],"has":[3],"demonstrated":[4],"significant":[5,216],"potential":[6],"for":[7,53],"automated":[8],"brain":[9],"metastases":[10],"(BM)":[11],"segmentation;":[12],"however,":[13],"models":[14],"trained":[15],"at":[16,24],"a":[17,46,65,142,215],"singular":[18],"institution":[19],"often":[20],"exhibit":[21],"suboptimal":[22],"performance":[23],"various":[25],"sites":[26],"due":[27],"to":[28,44,56,131,161,169,179,187,218],"disparities":[29],"in":[30],"scanner":[31],"hardware,":[32],"imaging":[33],"protocols,":[34],"and":[35,83,103,117,171,198,206],"patient":[36],"demographics.":[37],"The":[38,89,151],"goal":[39],"of":[40,222],"this":[41],"work":[42],"is":[43],"create":[45],"domain":[47,107,126],"adaptation":[48],"framework":[49],"that":[50,69],"will":[51],"allow":[52],"BM":[54,200],"segmentation":[55,201],"be":[57],"used":[58],"across":[59,137,182,203],"multiple":[60],"institutions.":[61,138],"Methods:":[62],"We":[63],"propose":[64],"VAE-MMD":[66,124,192],"preprocessing":[67],"pipeline":[68],"combines":[70],"variational":[71],"autoencoders":[72],"(VAE)":[73],"with":[74],"maximum":[75],"mean":[76,156,164,174],"discrepancy":[77],"(MMD)":[78],"loss,":[79],"incorporating":[80],"skip":[81],"connections":[82],"self-attention":[84],"mechanisms":[85],"alongside":[86],"nnU-Net":[87],"segmentation.":[88,224],"method":[90,153],"was":[91],"tested":[92],"on":[93],"740":[94],"patients":[95],"from":[96,129],"four":[97,184],"public":[98],"databases:":[99],"Stanford,":[100],"UCSF,":[101],"UCLM,":[102],"PKG,":[104],"evaluated":[105],"by":[106,158,166,176],"classifier's":[108],"accuracy,":[109],"sensitivity,":[110],"precision,":[111],"F1/F2":[112],"scores,":[113],"surface":[114],"Dice":[115],"(sDice),":[116],"95th":[118],"percentile":[119],"Hausdorff":[120],"distance":[121],"(HD95).":[122],"Results:":[123],"reduced":[125,172],"classifier":[127],"accuracy":[128],"0.91":[130],"0.50,":[132],"indicating":[133],"successful":[134],"feature":[135],"alignment":[136],"Reconstructed":[139],"volumes":[140],"attained":[141],"PSNR":[143],"greater":[144],"than":[145],"36":[146],"dB,":[147],"maintaining":[148],"anatomical":[149],"accuracy.":[150],"combined":[152],"raised":[154],"the":[155,163,173,188,219],"F1":[157],"11.1%":[159],"(0.700":[160],"0.778),":[162],"sDice":[165],"7.93%":[167],"(0.7121":[168],"0.7686),":[170],"HD95":[175],"65.5%":[177],"(11.33":[178],"3.91":[180],"mm)":[181],"all":[183],"centers":[185],"compared":[186],"baseline":[189],"nnU-Net.":[190],"Conclusions:":[191],"effectively":[193],"diminishes":[194],"cross-institutional":[195],"data":[196],"heterogeneity":[197],"enhances":[199],"generalization":[202],"volumetric,":[204],"detection,":[205],"boundary-level":[207],"metrics":[208],"without":[209],"necessitating":[210],"target-domain":[211],"labels,":[212],"thereby":[213],"overcoming":[214],"obstacle":[217],"clinical":[220],"implementation":[221],"AI-assisted":[223]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-04-03T00:00:00"}
