{"id":"https://openalex.org/W2903044280","doi":"https://doi.org/10.1109/icpr.2018.8545136","title":"Skin Lesion Segmentation via Dense Connected Deconvolutional Network","display_name":"Skin Lesion Segmentation via Dense Connected Deconvolutional Network","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903044280","doi":"https://doi.org/10.1109/icpr.2018.8545136","mag":"2903044280"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545136","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5100455165","display_name":"Hang Li","orcid":"https://orcid.org/0000-0003-4905-2242"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Li","raw_affiliation_strings":["School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058072674","display_name":"Xinzi He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinzi He","raw_affiliation_strings":["School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110569451","display_name":"Zhen Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhen Yu","raw_affiliation_strings":["School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047702220","display_name":"Feng Zhou","orcid":"https://orcid.org/0000-0001-6123-073X"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Zhou","raw_affiliation_strings":["Department of Industrial and Manufacturinz, Systems Enaineerinz, the University of Michizan, Dearborn, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Manufacturinz, Systems Enaineerinz, the University of Michizan, Dearborn, MI, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101455198","display_name":"Jie\u2010Zhi Cheng","orcid":"https://orcid.org/0000-0003-3446-8173"},"institutions":[{"id":"https://openalex.org/I4210135459","display_name":"United Imaging Healthcare (China)","ror":"https://ror.org/03qqw3m37","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210135459"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie-Zhi Cheng","raw_affiliation_strings":["United-Imaging Healthcare, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"United-Imaging Healthcare, Shanghai, China","institution_ids":["https://openalex.org/I4210135459"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072656839","display_name":"Li\u2010Min Huang","orcid":"https://orcid.org/0000-0002-9291-260X"},"institutions":[{"id":"https://openalex.org/I4210105608","display_name":"ShenZhen People\u2019s Hospital","ror":"https://ror.org/01hcefx46","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210105608"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limin Huang","raw_affiliation_strings":["The Operations Center of Shenzhen People's Hospital, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Operations Center of Shenzhen People's Hospital, Shenzhen, China","institution_ids":["https://openalex.org/I4210105608"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046543349","display_name":"Tianfu Wang","orcid":"https://orcid.org/0000-0002-1248-1214"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianfu Wang","raw_affiliation_strings":["School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001212991","display_name":"Baiying Lei","orcid":"https://orcid.org/0000-0002-3087-2550"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baiying Lei","raw_affiliation_strings":["School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.283,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.63294114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"671","last_page":"675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9919000267982483,"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/T11306","display_name":"Nonmelanoma Skin Cancer Studies","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/computer-science","display_name":"Computer science","score":0.7940925359725952},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7510107755661011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7212466597557068},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.6897830963134766},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6690629124641418},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6542098522186279},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5381492376327515},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5378649830818176},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5332120656967163},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5078328251838684},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4638082981109619},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42032167315483093},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.400126576423645},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21296587586402893},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15662232041358948},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.12031400203704834}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940925359725952},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7510107755661011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7212466597557068},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.6897830963134766},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6690629124641418},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6542098522186279},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5381492376327515},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5378649830818176},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5332120656967163},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5078328251838684},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4638082981109619},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42032167315483093},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.400126576423645},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21296587586402893},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15662232041358948},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12031400203704834},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545136","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1996828958","https://openalex.org/W2097117768","https://openalex.org/W2123080662","https://openalex.org/W2161236525","https://openalex.org/W2173520492","https://openalex.org/W2194775991","https://openalex.org/W2235523093","https://openalex.org/W2341999782","https://openalex.org/W2395611524","https://openalex.org/W2404812871","https://openalex.org/W2411541852","https://openalex.org/W2412320034","https://openalex.org/W2535388113","https://openalex.org/W2559597482","https://openalex.org/W2563705555","https://openalex.org/W2586172326","https://openalex.org/W2593946626","https://openalex.org/W2594873583","https://openalex.org/W2594970829","https://openalex.org/W2597394795","https://openalex.org/W2602631755","https://openalex.org/W2919115771","https://openalex.org/W2952793010","https://openalex.org/W2962808998","https://openalex.org/W2963373786","https://openalex.org/W2963446712","https://openalex.org/W2963591054","https://openalex.org/W2963946669","https://openalex.org/W3125108048","https://openalex.org/W4294568686","https://openalex.org/W4301462804","https://openalex.org/W4306369316","https://openalex.org/W6718379498","https://openalex.org/W6729060410","https://openalex.org/W6789640880"],"related_works":["https://openalex.org/W2559156603","https://openalex.org/W4300832495","https://openalex.org/W2987852271","https://openalex.org/W4287394948","https://openalex.org/W2967990525","https://openalex.org/W2949066288","https://openalex.org/W3119356360","https://openalex.org/W3202075396","https://openalex.org/W4214604401","https://openalex.org/W2368824897"],"abstract_inverted_index":{"Dermoscopy":[0],"imaging":[1],"analysis":[2],"is":[3,15,30,85,103,113,124,151],"a":[4,31,52],"routine":[5],"procedure":[6],"for":[7,24,58],"diagnosis":[8],"and":[9,41,66,79,130,135,143,160],"treatment":[10],"of":[11,43,73,92,132,169],"skin":[12,22,44,59,162],"lesions.":[13,45],"Segmentation":[14],"the":[16,89,93,99,110,141,147,156,167],"very":[17],"first":[18],"step":[19],"to":[20,35,87,105,115],"demarcate":[21],"lesions":[23],"further":[25],"quantitative":[26],"analysis.":[27],"However,":[28],"it":[29],"challenging":[32],"task":[33],"due":[34],"various":[36],"changes":[37],"from":[38],"different":[39],"viewpoints":[40],"scales":[42],"To":[46],"handle":[47],"these":[48],"challenges,":[49],"we":[50],"devise":[51],"new":[53],"dense":[54,76],"deconvolutional":[55],"network":[56,71],"(DDN)":[57],"lesion":[60,163],"segmentation":[61],"based":[62],"on":[63,155],"encoding":[64],"module":[65],"decoding":[67],"module.":[68],"Our":[69],"devised":[70],"consists":[72],"convolution":[74],"unit,":[75],"deconvolutionallayer":[77],"(DDL)":[78],"chained":[80,100],"residual":[81,101],"pooling":[82,102],"block.":[83],"DDL":[84],"adopted":[86],"restore":[88],"high":[90],"resolution":[91],"original":[94],"input":[95],"by":[96],"upsampling,":[97],"while":[98],"utilized":[104],"fuse":[106],"multilevel":[107],"features.":[108],"Also,":[109],"hierarchical":[111],"supervision":[112],"added":[114],"capture":[116],"low":[117],"level":[118],"detailed":[119],"boundary":[120],"information.":[121],"The":[122,153],"DDN":[123],"trained":[125],"in":[126],"an":[127],"end-to-end":[128],"manner":[129],"free":[131],"prior":[133],"knowledge":[134],"complicated":[136],"post-processing":[137],"procedures.":[138],"With":[139],"fusing":[140],"local":[142],"global":[144],"contextual":[145],"information,":[146],"high-resolution":[148],"prediction":[149],"output":[150],"obtained.":[152],"validation":[154],"public":[157],"ISBI":[158],"2016":[159],"2017":[161],"challenge":[164],"dataset":[165],"demonstrates":[166],"effectiveness":[168],"our":[170],"proposed":[171],"method.":[172]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
