{"id":"https://openalex.org/W7150786776","doi":"https://doi.org/10.48550/arxiv.2604.02868","title":"Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation","display_name":"Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7150786776","doi":"https://doi.org/10.48550/arxiv.2604.02868"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02868","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.2604.02868","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133027259","display_name":"Jie Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yang, Jie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133002033","display_name":"Ziqi Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Ziqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030651973","display_name":"Aihua Ke","orcid":"https://orcid.org/0000-0003-3638-7983"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ke, Aihua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133034494","display_name":"Jian Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Jian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133012904","display_name":"Bo Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Bo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133056843","display_name":"Xiaosong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaosong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5133027259"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8669999837875366,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8669999837875366,"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":0.031700000166893005,"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/T11019","display_name":"Image Enhancement Techniques","score":0.023399999365210533,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6827999949455261},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5938000082969666},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.5497999787330627},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.49810001254081726},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4927000105381012},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4916999936103821},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.454800009727478},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4230000078678131}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019000053405762},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6827999949455261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6304000020027161},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5938000082969666},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.5497999787330627},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.49810001254081726},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4916999936103821},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.454800009727478},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41179999709129333},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3336000144481659},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3059000074863434},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2946999967098236},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02868","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.2604.02868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02868","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data":[0],"heterogeneity":[1],"hinders":[2],"clinical":[3],"deployment":[4],"of":[5,77,87,128,142,160,167,184],"medical":[6],"image":[7,62],"analysis":[8],"models,":[9],"and":[10,32,37,68,116,177,186],"generative":[11],"data":[12,104],"augmentation":[13],"helps":[14],"mitigate":[15],"this":[16,47],"issue.":[17],"However,":[18],"recent":[19],"diffusion-based":[20],"methods":[21],"that":[22,56],"synthesize":[23],"image-mask":[24],"pairs":[25],"often":[26],"ignore":[27],"distribution":[28,63,113,127],"shifts":[29],"between":[30],"generated":[31,123,143],"real":[33],"images":[34,79,124],"across":[35,181],"scenarios,":[36],"such":[38],"mismatches":[39],"can":[40],"markedly":[41],"degrade":[42],"downstream":[43],"performance.":[44],"To":[45],"address":[46],"issue,":[48],"we":[49,83,110,145],"propose":[50],"AlignFlow,":[51],"a":[52,74,112,148,182],"flow":[53,89,149],"matching":[54,90,150],"model":[55,91,100],"aligns":[57],"with":[58],"the":[59,85,88,96,99,102,122,126,129,133,140,156,165],"target":[60,134],"reference":[61,78],"via":[64],"differentiable":[65,118],"reward":[66,119],"fine-tuning,":[67],"remains":[69],"effective":[70],"even":[71],"when":[72],"only":[73],"small":[75],"number":[76],"are":[80],"provided.":[81],"Specifically,":[82],"divide":[84],"training":[86,103],"into":[92],"two":[93],"stages:":[94],"in":[95,158,175,179],"first":[97],"stage,":[98],"fits":[101],"to":[105,120,138,154],"generate":[106],"plausible":[107],"images;":[108],"Then,":[109],"introduce":[111],"alignment":[114],"mechanism":[115],"employ":[117],"steer":[121],"toward":[125],"given":[130],"samples":[131],"from":[132],"domain.":[135],"In":[136],"addition,":[137],"enhance":[139],"diversity":[141,157],"masks,":[144],"also":[146],"design":[147],"based":[151],"mask":[152],"generation":[153],"complement":[155],"regions":[159],"interest.":[161],"Extensive":[162],"experiments":[163],"demonstrate":[164],"effectiveness":[166],"our":[168],"approach,":[169],"i.e.,":[170],"performance":[171],"improvement":[172],"by":[173],"3.5-4.0%":[174],"mDice":[176],"3.5-5.6%":[178],"mIoU":[180],"variety":[183],"datasets":[185],"scenarios.":[187]},"counts_by_year":[],"updated_date":"2026-04-07T06:06:30.997549","created_date":"2026-04-07T00:00:00"}