{"id":"https://openalex.org/W7126090081","doi":"https://doi.org/10.1109/bibm66473.2025.11356230","title":"A-Mel: A Resource-Efficient Deep Learning Agent for Precise Early Melanoma Diagnosis","display_name":"A-Mel: A Resource-Efficient Deep Learning Agent for Precise Early Melanoma Diagnosis","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126090081","doi":"https://doi.org/10.1109/bibm66473.2025.11356230"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356230","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":"conference-paper","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/A5124232755","display_name":"Yaoyu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoyu Liu","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457929","display_name":"Jiawen Zhang","orcid":"https://orcid.org/0000-0003-1607-1375"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jiawen Zhang","raw_affiliation_strings":["School of Computer Science, The University of Sydney,Sydney,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney,Sydney,Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124205614","display_name":"Yingjie Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yingjie Cao","raw_affiliation_strings":["School of Computer Science, The University of Sydney,Sydney,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney,Sydney,Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124237338","display_name":"Denan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Denan Liu","raw_affiliation_strings":["School of Computer Science, The University of Sydney,Sydney,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney,Sydney,Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103835960","display_name":"Jie Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jie Chang","raw_affiliation_strings":["School of Computer Science, The University of Sydney,Sydney,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney,Sydney,Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124180573","display_name":"Sike Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sike Chen","raw_affiliation_strings":["School of Economics and Management, Beijing Forestry University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing Forestry University,Beijing,China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124257281","display_name":"Shaoliang Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoliang Peng","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"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":"2508","last_page":"2515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.004100000020116568,"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/T11448","display_name":"Face recognition and analysis","score":0.003700000001117587,"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/pooling","display_name":"Pooling","score":0.7401999831199646},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6552000045776367},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5539000034332275},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5346999764442444},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4875999987125397},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.47049999237060547},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4438000023365021},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3865000009536743},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.38429999351501465}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7448999881744385},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7401999831199646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7102000117301941},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6552000045776367},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5539000034332275},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4438000023365021},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3865000009536743},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.375900000333786},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.32170000672340393},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C68767595","wikidata":"https://www.wikidata.org/wiki/Q1677999","display_name":"Contiguity","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C2777108052","wikidata":"https://www.wikidata.org/wiki/Q3137263","display_name":"Electrochemotherapy","level":4,"score":0.3138999938964844},{"id":"https://openalex.org/C2777658100","wikidata":"https://www.wikidata.org/wiki/Q180614","display_name":"Melanoma","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2778324318","wikidata":"https://www.wikidata.org/wiki/Q902923","display_name":"Dermatoscopy","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356230","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":41,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1974933801","https://openalex.org/W2003834444","https://openalex.org/W2004999523","https://openalex.org/W2023574219","https://openalex.org/W2038106698","https://openalex.org/W2039233216","https://openalex.org/W2047433299","https://openalex.org/W2051379654","https://openalex.org/W2106005668","https://openalex.org/W2120807471","https://openalex.org/W2151287908","https://openalex.org/W2164273268","https://openalex.org/W2194775991","https://openalex.org/W2493767331","https://openalex.org/W2498889855","https://openalex.org/W2581082771","https://openalex.org/W2592929672","https://openalex.org/W2607041014","https://openalex.org/W2903633560","https://openalex.org/W2963073614","https://openalex.org/W2982083293","https://openalex.org/W3011743383","https://openalex.org/W3012192396","https://openalex.org/W3015788359","https://openalex.org/W3025800305","https://openalex.org/W3026858009","https://openalex.org/W3029816221","https://openalex.org/W3034238686","https://openalex.org/W3083291461","https://openalex.org/W3094502228","https://openalex.org/W3108316907","https://openalex.org/W3120894151","https://openalex.org/W3121732873","https://openalex.org/W3138516171","https://openalex.org/W4312453532","https://openalex.org/W4320481841","https://openalex.org/W4361270341","https://openalex.org/W4393935425","https://openalex.org/W4403854970","https://openalex.org/W4409072772"],"related_works":[],"abstract_inverted_index":{"Early":[0],"and":[1,22,63,86,112,152,188],"precise":[2,42],"diagnosis":[3],"of":[4,139,167,185],"melanoma":[5,44,64],"significantly":[6,82],"improves":[7],"patient":[8],"prognosis.":[9],"However,":[10],"current":[11],"diagnostic":[12],"methods":[13],"are":[14,201],"challenged":[15],"by":[16],"hair":[17,56,145],"occlusion,":[18],"diverse":[19],"lesion":[20,61],"morphologies,":[21],"limited":[23],"computational":[24,87],"resources":[25],"in":[26,183],"clinical":[27],"settings.":[28],"In":[29],"this":[30],"paper,":[31],"we":[32,67,90],"propose":[33],"A-Mel,":[34],"a":[35,48,69,92,136,162],"resource-efficient":[36],"deep":[37],"learning":[38],"agent":[39,50],"designed":[40],"for":[41,193,199],"early":[43],"diagnosis.":[45],"A-Mel":[46,159,200],"integrates":[47],"unified":[49],"framework":[51],"comprising":[52],"three":[53],"sequential":[54],"modules:":[55],"artifact":[57],"removal":[58],"preprocessing,":[59],"pathological":[60],"segmentation,":[62],"classification.":[65],"Specifically,":[66],"employ":[68],"modified":[70],"<tex":[71],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[72],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{U}^{2}":[73],"\\text{Net}++$</tex>":[74],"architecture":[75],"enhanced":[76],"with":[77,100,119,144,190],"depthwise":[78],"separable":[79],"convolutions":[80],"to":[81,127],"reduce":[83],"model":[84,160,179],"parameters":[85],"load.":[88],"Furthermore,":[89],"introduce":[91],"novel":[93],"Dual-Path":[94],"Spatial":[95,122],"Attention":[96,103],"(DPSA)":[97],"mechanism":[98],"integrated":[99],"Efficient":[101],"Channel":[102],"(ECA),":[104],"achieving":[105],"comprehensive":[106],"attention":[107],"enhancement":[108],"across":[109],"multiple":[110],"scales":[111],"dimensions.":[113],"The":[114,197],"network":[115],"is":[116,180],"further":[117],"augmented":[118],"an":[120],"Atrous":[121],"Pyramid":[123],"Pooling":[124],"(ASPP)":[125],"module":[126],"strengthen":[128],"multi-scale":[129],"feature":[130],"representation.":[131],"Experimental":[132],"evaluations":[133],"conducted":[134],"on":[135],"test":[137],"set":[138],"approximately":[140],"700":[141],"dermoscopic":[142],"images":[143],"artifacts":[146],"selected":[147],"from":[148],"the":[149],"ISIC":[150,153],"2019":[151],"2020":[154],"datasets":[155],"demonstrate":[156],"that":[157],"our":[158],"achieves":[161],"state-of-the-art":[163],"binary":[164],"classification":[165],"accuracy":[166],"99.12":[168],"%,":[169],"outperforming":[170],"existing":[171],"approaches":[172],"through":[173],"extensive":[174],"comparative":[175],"analyses.":[176],"Our":[177],"segmentation":[178],"\u201cextremely":[181],"lightweight\u201d":[182],"terms":[184],"parameters,":[186],"volume,":[187],"speed,":[189],"excellent":[191],"performance":[192],"edge":[194],"deployment":[195],"scenarios.":[196],"codes":[198],"made":[202],"publicly":[203],"available":[204],"at":[205],"https://github.com/Yaoooyu/A-Mel.":[206]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}