{"id":"https://openalex.org/W7126051266","doi":"https://doi.org/10.1109/bibm66473.2025.11356306","title":"A Diffusion and Spatial Prototype Learning Method for Semi-Supervised Medical Image Segmentation","display_name":"A Diffusion and Spatial Prototype Learning Method for Semi-Supervised Medical Image Segmentation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126051266","doi":"https://doi.org/10.1109/bibm66473.2025.11356306"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356306","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/A5113342692","display_name":"Lianyuan Yu","orcid":"https://orcid.org/0009-0001-5350-4283"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lianyuan Yu","raw_affiliation_strings":["School of Mathematical Sciences, Capital Normal University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087214799","display_name":"Xiuzhen Guo","orcid":"https://orcid.org/0009-0007-9295-124X"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuzhen Guo","raw_affiliation_strings":["School of Mathematical Sciences, Capital Normal University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069289849","display_name":"X. Y. Zhao","orcid":"https://orcid.org/0009-0004-1367-0692"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuying Zhao","raw_affiliation_strings":["School of Mathematical Sciences, Capital Normal University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065859286","display_name":"Hui Li","orcid":"https://orcid.org/0000-0001-9198-3951"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Li","raw_affiliation_strings":["School of Mathematical Sciences, Capital Normal University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5113342692"],"corresponding_institution_ids":["https://openalex.org/I96852419"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69076081,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4359","last_page":"4364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6624000072479248,"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":0.6624000072479248,"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":0.07259999960660934,"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":0.04179999977350235,"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":0.6603000164031982},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5627999901771545},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5214999914169312},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5101000070571899},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5054000020027161},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4909000098705292},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.48500001430511475},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42640000581741333},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41679999232292175},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41429999470710754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7443000078201294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7365000247955322},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6603000164031982},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5627999901771545},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5214999914169312},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5054000020027161},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4909000098705292},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.48500001430511475},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4587000012397766},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.39259999990463257},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3366999924182892},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.31209999322891235},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27390000224113464},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2680000066757202}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356306","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":[{"id":"https://openalex.org/G1237570396","display_name":null,"funder_award_id":"L242127","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2051027313","https://openalex.org/W2396622801","https://openalex.org/W2616240092","https://openalex.org/W2921629474","https://openalex.org/W2962914239","https://openalex.org/W2979907638","https://openalex.org/W3044738063","https://openalex.org/W3083779147","https://openalex.org/W3093394156","https://openalex.org/W3165010883","https://openalex.org/W3203046931","https://openalex.org/W4282935002","https://openalex.org/W4285040393","https://openalex.org/W4310466409","https://openalex.org/W4382404966","https://openalex.org/W4386075748","https://openalex.org/W4387211674","https://openalex.org/W4389315412","https://openalex.org/W4392013871","https://openalex.org/W4400839237","https://openalex.org/W4403598804","https://openalex.org/W4403619442","https://openalex.org/W4404815343"],"related_works":[],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1,83],"performs":[2],"accurate":[3],"segmentation":[4],"by":[5,28],"effectively":[6],"leveraging":[7],"unlabeled":[8],"data.":[9],"Most":[10,49],"existing":[11],"methods":[12,51],"struggle":[13],"to":[14,40,119,142],"capture":[15],"complex":[16],"global":[17,149],"data":[18,122],"distribution,":[19],"which":[20],"is":[21,111,176],"particularly":[22],"important":[23],"for":[24,86],"medical":[25,88,157],"images":[26],"characterized":[27],"intricate":[29],"background":[30],"structures":[31],"and":[32,69,80,124,136,147,162],"ambiguous":[33],"boundaries.":[34],"Class":[35],"prototypes":[36,129],"are":[37,130],"commonly":[38],"used":[39],"represent":[41],"the":[42,92,104,108,133,139],"essential":[43],"semantic":[44],"representation":[45],"of":[46],"each":[47],"class.":[48],"prototype":[50,55,82],"construct":[52],"a":[53,78,114],"single":[54],"vector":[56],"per":[57],"class":[58,128],"using":[59],"convolutional":[60],"neural":[61],"networks":[62],"(CNNs),":[63],"thereby":[64],"neglecting":[65],"both":[66],"distribution":[67,101,123,150],"modeling":[68],"intra-class":[70],"variability.":[71],"To":[72],"address":[73],"these":[74],"issues,":[75],"we":[76],"propose":[77],"diffusion":[79,109,140],"spatial":[81],"method":[84,168],"(DSP)":[85],"semi-supervised":[87],"image":[89,158],"segmentation.":[90],"Firstly,":[91],"Image-to-Segmentation":[93],"Diffusion":[94],"Model":[95],"(ISDM)":[96],"generates":[97],"pseudo-labels":[98],"rich":[99],"in":[100],"information":[102],"from":[103],"original":[105],"image.":[106],"Secondly,":[107],"network":[110],"combined":[112,137],"with":[113,138],"Convolutional":[115],"Segmentation":[116],"Network":[117],"(CSN)":[118],"simultaneously":[120],"learn":[121],"anatomical":[125],"details.":[126],"Thirdly,":[127],"constructed":[131],"at":[132],"voxel":[134],"level":[135],"model":[141],"make":[143],"them":[144],"spatially":[145],"aware":[146],"incorporate":[148],"information.":[151],"Extensive":[152],"experiments":[153],"on":[154],"three":[155],"benchmark":[156],"datasets":[159],"(left":[160],"atrium":[161],"brain":[163],"tumor)":[164],"show":[165],"that":[166],"our":[167],"achieves":[169],"state-of-the-art":[170],"performance,":[171],"validating":[172],"its":[173],"effectiveness.":[174],"Code":[175],"available":[177],"at:":[178],"https://github.com/Lianyuan-Yu/DSP.":[179]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}