{"id":"https://openalex.org/W7126101507","doi":"https://doi.org/10.1109/bibm66473.2025.11356434","title":"Diffusion-Based Pre-Training for Label-Efficient Abdominal Multi-Organ Segmentation","display_name":"Diffusion-Based Pre-Training for Label-Efficient Abdominal Multi-Organ Segmentation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126101507","doi":"https://doi.org/10.1109/bibm66473.2025.11356434"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356434","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124221096","display_name":"Yongzhi Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yongzhi Huang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124276214","display_name":"Jinxin Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinxin Zhu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haseeb Hassan","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haseeb Hassan","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364802","display_name":"Jin Li","orcid":"https://orcid.org/0000-0002-6131-456X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Li","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124153868","display_name":"Liyilei Su","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liyilei Su","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015878061","display_name":"Bingding Huang","orcid":"https://orcid.org/0000-0002-4748-2882"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingding Huang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5124221096"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71130465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.3034000098705292,"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":0.3034000098705292,"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/T10862","display_name":"AI in cancer detection","score":0.21220000088214874,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.1404999941587448,"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/segmentation","display_name":"Segmentation","score":0.722599983215332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4659000039100647},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45399999618530273},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.4016000032424927},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3763999938964844},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.3465000092983246}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7319999933242798},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.722599983215332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6521000266075134},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45399999618530273},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.32429999113082886},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.314300000667572},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356434","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2964309882","https://openalex.org/W2996290406","https://openalex.org/W3007268491","https://openalex.org/W3014974815","https://openalex.org/W3036586801","https://openalex.org/W3096831136","https://openalex.org/W3159481202","https://openalex.org/W3171581326","https://openalex.org/W3173143063","https://openalex.org/W3182978476","https://openalex.org/W4212875960","https://openalex.org/W4221163766","https://openalex.org/W4286220311","https://openalex.org/W4292829030","https://openalex.org/W4312740349","https://openalex.org/W4312749295","https://openalex.org/W4313156423","https://openalex.org/W4321194933","https://openalex.org/W4387580644","https://openalex.org/W4401725766"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"multi-organ":[1],"segmentation":[2,32,91],"in":[3,37,138,184],"Computed":[4],"Tomography":[5],"(CT)":[6],"images":[7,115],"is":[8,66],"critical":[9],"for":[10,34,73,90,133],"computer-aided":[11],"diagnosis":[12],"systems.":[13],"However,":[14],"existing":[15],"supervised":[16,189],"methods":[17],"heavily":[18],"rely":[19],"on":[20,55,69,128,158,191],"costly,":[21],"high-quality":[22],"labeled":[23,71,140,148,167,193],"data.":[24,194],"To":[25],"address":[26],"this,":[27],"we":[28,49],"propose":[29],"a":[30,44,51,174],"label-efficient":[31],"method":[33,124,151,171,183],"abdominal":[35,74,160],"organs":[36],"CT":[38,59,114],"images,":[39],"leveraging":[40],"knowledge":[41],"transfer":[42],"from":[43],"pre-trained":[45,101],"diffusion":[46,53,102],"model.":[47],"Specifically,":[48],"pre-train":[50],"denoising":[52],"model":[54,89,103],"207,029":[56],"unlabeled":[57],"2D":[58,168],"slices":[60],"to":[61,86],"capture":[62],"anatomical":[63],"patterns,":[64],"which":[65],"then":[67],"fine-tuned":[68],"limited":[70,139],"data":[72,141],"organ":[75,134],"segmentation.":[76],"During":[77],"fine-tuning,":[78],"two":[79],"strate-gies-linear":[80],"probing":[81],"and":[82,107,146,156],"decoder":[83],"fine-tuning-are":[84],"employed":[85],"adapt":[87],"the":[88,100,129,179,186],"while":[92],"preserving":[93],"learned":[94],"representations.":[95],"Quantitative":[96],"results":[97],"demonstrate":[98],"that":[99],"can":[104],"generate":[105],"diverse":[106],"realistic":[108],"<tex":[109],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[110],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$256":[111],"\\times":[112],"256$</tex>":[113],"(FID:":[116],"11.32,":[117],"sFID:":[118],"46.93,":[119],"F1-score:":[120],"73.1%).":[121],"Moreover,":[122],"our":[123,150,170,182],"achieves":[125,152,173],"competitive":[126],"performance":[127],"FLARE":[130],"2022":[131],"dataset":[132],"segmentation,":[135],"particularly":[136],"excelling":[137],"scenarios.":[142],"With":[143],"only":[144,165],"10%":[145],"1%":[147],"data,":[149],"DSCs":[153],"of":[154,176,181,188],"78.51%":[155],"71.56%":[157],"13":[159],"organs,":[161],"respectively.":[162],"Remarkably,":[163],"with":[164],"four":[166],"slices,":[169],"still":[172],"DSC":[175],"51.81%,":[177],"highlighting":[178],"efficacy":[180],"alleviating":[185],"reliance":[187],"learning":[190],"large-scale":[192]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-01-30T00:00:00"}