{"id":"https://openalex.org/W4416252359","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227438","title":"MTPKDistillNet: Multi-Teacher Prior Knowledge Distillation Network for Lung Tumor Surgical Section Image Classification","display_name":"MTPKDistillNet: Multi-Teacher Prior Knowledge Distillation Network for Lung Tumor Surgical Section Image Classification","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252359","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227438"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227438","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227438","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/A5043533818","display_name":"Yonglin Peng","orcid":"https://orcid.org/0000-0003-3446-3030"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yonglin Peng","raw_affiliation_strings":["South China Normal University,School of Computer Science,Guangzhou,China,510631"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Computer Science,Guangzhou,China,510631","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103216203","display_name":"Qizhi Huang","orcid":"https://orcid.org/0000-0002-4865-5669"},"institutions":[{"id":"https://openalex.org/I4210145693","display_name":"Guangdong Academy of Medical Sciences","ror":"https://ror.org/0432p8t34","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210145693"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qizhi Huang","raw_affiliation_strings":["Guangdong Academy of Medical Sciences,Guangdong Provincial People’s Hospital"],"affiliations":[{"raw_affiliation_string":"Guangdong Academy of Medical Sciences,Guangdong Provincial People’s Hospital","institution_ids":["https://openalex.org/I4210145693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040190558","display_name":"Chengchuang Lin","orcid":"https://orcid.org/0000-0002-3514-2071"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengchuang Lin","raw_affiliation_strings":["South China Normal University,School of Computer Science,Guangzhou,China,510631"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Computer Science,Guangzhou,China,510631","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101818721","display_name":"Gansen Zhao","orcid":"https://orcid.org/0009-0007-1526-5326"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gansen Zhao","raw_affiliation_strings":["South China Normal University,School of Computer Science,Guangzhou,China,510631"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Computer Science,Guangzhou,China,510631","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016101422","display_name":"Haiyu Zhou","orcid":"https://orcid.org/0000-0002-3328-6792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haiyu Zhou","raw_affiliation_strings":["Guangdong Cardiovascular Institute"],"affiliations":[{"raw_affiliation_string":"Guangdong Cardiovascular Institute","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043533818"],"corresponding_institution_ids":["https://openalex.org/I187400657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.488226,"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":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.5496000051498413,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.5496000051498413,"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/T10862","display_name":"AI in cancer detection","score":0.21649999916553497,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.05660000070929527,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6826000213623047},{"id":"https://openalex.org/keywords/lung-cancer","display_name":"Lung cancer","score":0.4796000123023987},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4779999852180481},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45579999685287476},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.45019999146461487},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.38929998874664307},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.37529999017715454},{"id":"https://openalex.org/keywords/surgical-planning","display_name":"Surgical planning","score":0.3391999900341034},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.3165000081062317}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6826000213623047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5924999713897705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5763999819755554},{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.45019999146461487},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.38929998874664307},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36640000343322754},{"id":"https://openalex.org/C2779370443","wikidata":"https://www.wikidata.org/wiki/Q1776627","display_name":"Surgical planning","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33090001344680786},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3043999969959259},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C2780129039","wikidata":"https://www.wikidata.org/wiki/Q1931107","display_name":"Section (typography)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2978000044822693},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C201645570","wikidata":"https://www.wikidata.org/wiki/Q830637","display_name":"Radiation treatment planning","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227438","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227438","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":[{"id":"https://openalex.org/F4320335869","display_name":"National Social Science Fund of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2003048812","https://openalex.org/W2051183700","https://openalex.org/W2179117785","https://openalex.org/W2194775991","https://openalex.org/W2765793020","https://openalex.org/W2794825826","https://openalex.org/W2800768023","https://openalex.org/W2888570268","https://openalex.org/W2936864631","https://openalex.org/W2963140444","https://openalex.org/W2963691377","https://openalex.org/W2982083293","https://openalex.org/W3004014454","https://openalex.org/W3035834839","https://openalex.org/W3112252674","https://openalex.org/W3113359627","https://openalex.org/W3177196641","https://openalex.org/W3204971388","https://openalex.org/W4226426325","https://openalex.org/W4289752563","https://openalex.org/W4295016021","https://openalex.org/W4306955476","https://openalex.org/W4312349930","https://openalex.org/W4312443924","https://openalex.org/W4316035260","https://openalex.org/W4319004953","https://openalex.org/W4362496574","https://openalex.org/W4367303100","https://openalex.org/W4386076493","https://openalex.org/W4386849736","https://openalex.org/W4389611927","https://openalex.org/W4390617198","https://openalex.org/W4390802254","https://openalex.org/W4393002189","https://openalex.org/W4393935425","https://openalex.org/W4394821350","https://openalex.org/W4395451426","https://openalex.org/W4402351476","https://openalex.org/W4402352973","https://openalex.org/W4403334864"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"identification":[1],"of":[2,13,28,103,142,180,200],"lung":[3,29,39,104,188],"cancer":[4,40,105,189],"subtypes":[5],"is":[6],"critical":[7],"for":[8,23,100],"the":[9,138,143,147,156,163,167,172,177,181],"diagnosis":[10],"and":[11,36,80,121,151,166],"treatment":[12],"pulmonary":[14],"lesions":[15],"in":[16,34],"clinical":[17,187],"practice.":[18],"Deep":[19],"learning-based":[20],"diagnostic":[21,178],"systems":[22],"surgical":[24,107,190],"lesion":[25,108],"section":[26,109],"images":[27],"tumors":[30],"can":[31],"assist":[32],"clinicians":[33],"efficiently":[35],"accurately":[37],"recognizing":[38],"subtypes,":[41],"enabling":[42],"targeted":[43],"therapies.":[44],"However,":[45],"existing":[46,73],"methods":[47,74],"often":[48],"fail":[49],"to":[50,61,118,136,159],"fully":[51],"leverage":[52],"prior":[53,115],"knowledge":[54,116],"from":[55,63,76,106,124,132,171],"medical":[56],"images,":[57],"making":[58],"them":[59],"susceptible":[60],"interference":[62],"high-intensity":[64],"light":[65],"regions":[66],"caused":[67],"by":[68],"external":[69],"illumination.":[70],"Moreover,":[71],"many":[72],"suffer":[75],"high":[77],"computational":[78],"complexity":[79],"excessive":[81],"parameters,":[82],"which":[83],"hinder":[84],"their":[85],"practical":[86],"application.":[87],"To":[88],"address":[89],"these":[90],"challenges,":[91],"this":[92],"paper":[93],"proposes":[94],"MTPKDistillNet,":[95],"a":[96,114,186,197],"novel":[97],"framework":[98,112,148],"designed":[99],"subtype":[101],"recognition":[102],"images.":[110,125],"The":[111],"incorporates":[113],"module":[117],"extract":[119],"edge":[120],"texture":[122],"features":[123],"It":[126],"then":[127],"performs":[128],"multi-level":[129],"information":[130,170],"distillation":[131],"multiple":[133],"teacher":[134,173],"networks":[135],"enhance":[137],"feature":[139],"extraction":[140],"capability":[141,179],"student":[144,157],"network.":[145,174],"Finally,":[146],"employs":[149],"decoupled":[150],"category":[152],"relation":[153],"distillation,":[154],"allowing":[155],"network":[158],"effectively":[160],"learn":[161],"both":[162],"predicted":[164],"outputs":[165],"class":[168],"relevance":[169],"This":[175],"enhances":[176],"model.":[182],"Experimental":[183],"results":[184],"on":[185],"slide":[191],"dataset":[192],"demonstrate":[193],"that":[194],"MTPKDistillNet":[195],"achieves":[196],"classification":[198],"accuracy":[199],"87.55":[201]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}