{"id":"https://openalex.org/W4416249693","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228818","title":"Region Contrast Distillation for Medical Image Segmentation","display_name":"Region Contrast Distillation for Medical Image Segmentation","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249693","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228818"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5109989487","display_name":"Jincheng Pu","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiahao Pu","raw_affiliation_strings":["Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China"],"affiliations":[{"raw_affiliation_string":"Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078309721","display_name":"Wanli Dong","orcid":"https://orcid.org/0000-0003-2680-3736"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanli Dong","raw_affiliation_strings":["Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China"],"affiliations":[{"raw_affiliation_string":"Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112873022","display_name":"Jiachuan Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiachuan Fan","raw_affiliation_strings":["Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China"],"affiliations":[{"raw_affiliation_string":"Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101759345","display_name":"Ning Jiang","orcid":"https://orcid.org/0000-0003-0794-7996"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Jiang","raw_affiliation_strings":["Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China"],"affiliations":[{"raw_affiliation_string":"Southwest University of Science and Technology,School of Computer Science and Technology,Mianyang,China","institution_ids":["https://openalex.org/I1297991670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5109989487"],"corresponding_institution_ids":["https://openalex.org/I1297991670"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37214718,"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":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8863000273704529,"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":0.8863000273704529,"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":0.015200000256299973,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.013899999670684338,"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/segmentation","display_name":"Segmentation","score":0.6435999870300293},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5845999717712402},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5587000250816345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5565000176429749},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5527999997138977},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.550599992275238},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44350001215934753},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4343999922275543},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.41339999437332153}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7263000011444092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7128999829292297},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6435999870300293},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5587000250816345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5527999997138977},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4343999922275543},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41339999437332153},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41190001368522644},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3693000078201294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3596999943256378},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34599998593330383},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2858000099658966},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.26109999418258667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":27,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2762439315","https://openalex.org/W2791680898","https://openalex.org/W2884436604","https://openalex.org/W2899279931","https://openalex.org/W2907750714","https://openalex.org/W2952787292","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963418739","https://openalex.org/W2964217532","https://openalex.org/W3015788359","https://openalex.org/W3105676814","https://openalex.org/W3164443777","https://openalex.org/W3185556852","https://openalex.org/W4214524539","https://openalex.org/W4225165925","https://openalex.org/W4312309807","https://openalex.org/W4312980183","https://openalex.org/W4321232185","https://openalex.org/W4382458595","https://openalex.org/W4387211518","https://openalex.org/W4402667890","https://openalex.org/W4402727442","https://openalex.org/W4404892995","https://openalex.org/W6902762063"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,24],"deep":[3],"learning":[4],"have":[5],"significantly":[6,168],"boosted":[7],"medical":[8,157,194],"image":[9,184,195],"segmentation,":[10],"but":[11],"the":[12,54,62,76,87,92,95,102,106,118,134,139,170],"increasing":[13],"number":[14],"of":[15,65,72,78,94,98,101,108,111,172],"parameters":[16],"and":[17,81,85,162,176,192],"computations":[18],"pose":[19],"challenges":[20],"for":[21,183],"deploying":[22],"models":[23,175],"resource-constrained":[25,198],"environments.":[26],"To":[27],"address":[28],"this":[29],"issue,":[30],"we":[31,52,116],"propose":[32],"an":[33],"effective":[34],"structured":[35,88],"knowledge":[36,180],"transfer":[37],"framework":[38],"that":[39,147,165],"uses":[40],"a":[41,47],"well-performing":[42],"teacher":[43],"model":[44,104,141],"to":[45,128,142,150,189],"train":[46],"lightweight":[48,173],"student":[49,140,174],"model.":[50],"Specifically,":[51],"present":[53],"region":[55],"comparison":[56],"distillation(RCD)":[57],"method,":[58],"which":[59],"first":[60],"encodes":[61],"internal":[63],"information":[64,89],"different":[66,109],"semantic":[67],"regions":[68,146],"into":[69],"various":[70],"classes":[71,110],"feature":[73,99,112],"vectors":[74,100],"with":[75,105],"help":[77],"auxiliary":[79],"labels":[80],"then":[82],"efficiently":[83],"extracts":[84],"conveys":[86],"by":[90,124],"comparing":[91],"similarity":[93],"same":[96],"kind":[97],"teacher-student":[103],"difference":[107],"vectors.":[113],"In":[114],"addition,":[115],"improve":[117],"traditional":[119],"logit":[120],"layer":[121],"distillation":[122,181],"method":[123,167],"dynamically":[125],"assigning":[126],"weights":[127],"each":[129],"pixel":[130],"point":[131],"when":[132],"calculating":[133],"KL":[135],"scatter":[136],"loss,":[137],"allowing":[138],"focus":[143],"more":[144],"on":[145,155,197],"are":[148],"difficult":[149],"segment.":[151],"Extensive":[152],"experimental":[153],"results":[154],"three":[156],"segmentation":[158,196],"datasets,":[159],"BTCV,":[160],"Synapse,":[161],"Bladder,":[163],"show":[164],"our":[166],"improves":[169],"performance":[171],"outperforms":[177],"current":[178],"state-of-the-art":[179],"methods":[182],"segmentation.":[185],"It":[186],"is":[187],"expected":[188],"achieve":[190],"efficient":[191],"real-time":[193],"devices.":[199]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}