{"id":"https://openalex.org/W2674824053","doi":"https://doi.org/10.1109/isbi.2017.7950514","title":"Investigating deep side layers for skin lesion segmentation","display_name":"Investigating deep side layers for skin lesion segmentation","publication_year":2017,"publication_date":"2017-04-01","ids":{"openalex":"https://openalex.org/W2674824053","doi":"https://doi.org/10.1109/isbi.2017.7950514","mag":"2674824053"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2017.7950514","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2017.7950514","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","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/A5058274549","display_name":"Behzad Bozorgtabar","orcid":"https://orcid.org/0000-0002-5759-4896"},"institutions":[{"id":"https://openalex.org/I4210120068","display_name":"IBM Research - Australia","ror":"https://ror.org/027r3nx49","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210120068"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Behzad Bozorgtabar","raw_affiliation_strings":["IBM Research - Australia, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Australia, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I4210120068"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005014252","display_name":"Zongyuan Ge","orcid":"https://orcid.org/0000-0002-5880-8673"},"institutions":[{"id":"https://openalex.org/I4210120068","display_name":"IBM Research - Australia","ror":"https://ror.org/027r3nx49","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210120068"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zongyuan Ge","raw_affiliation_strings":["IBM Research - Australia, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Australia, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I4210120068"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017120967","display_name":"Rajib Chakravorty","orcid":"https://orcid.org/0000-0001-7629-5894"},"institutions":[{"id":"https://openalex.org/I4210120068","display_name":"IBM Research - Australia","ror":"https://ror.org/027r3nx49","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210120068"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rajib Chakravorty","raw_affiliation_strings":["IBM Research - Australia, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Australia, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I4210120068"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110142512","display_name":"Mani Abedini","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120068","display_name":"IBM Research - Australia","ror":"https://ror.org/027r3nx49","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210120068"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mani Abedini","raw_affiliation_strings":["IBM Research - Australia, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Australia, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I4210120068"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043115498","display_name":"Sergey Demyanov","orcid":"https://orcid.org/0000-0002-3047-5387"},"institutions":[{"id":"https://openalex.org/I4210120068","display_name":"IBM Research - Australia","ror":"https://ror.org/027r3nx49","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210120068"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sergey Demyanov","raw_affiliation_strings":["IBM Research - Australia, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Australia, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I4210120068"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091677392","display_name":"Rahil Garnavi","orcid":"https://orcid.org/0000-0002-3844-6361"},"institutions":[{"id":"https://openalex.org/I4210120068","display_name":"IBM Research - Australia","ror":"https://ror.org/027r3nx49","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210120068"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rahil Garnavi","raw_affiliation_strings":["IBM Research - Australia, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - Australia, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I4210120068"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210120068"],"apc_list":null,"apc_paid":null,"fwci":2.9251,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.92067308,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"256","last_page":"260"},"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/T11306","display_name":"Nonmelanoma Skin Cancer Studies","score":0.9804999828338623,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9732999801635742,"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":0.8496301174163818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7751399278640747},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7191667556762695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.697091817855835},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.6888121366500854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6258247494697571},{"id":"https://openalex.org/keywords/skin-lesion","display_name":"Skin lesion","score":0.5782735347747803},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5332524180412292},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4723162353038788},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43515336513519287},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40674829483032227},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15164276957511902},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.08157563209533691}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8496301174163818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7751399278640747},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7191667556762695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697091817855835},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.6888121366500854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6258247494697571},{"id":"https://openalex.org/C2988168687","wikidata":"https://www.wikidata.org/wiki/Q949302","display_name":"Skin lesion","level":2,"score":0.5782735347747803},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5332524180412292},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4723162353038788},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43515336513519287},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40674829483032227},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15164276957511902},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.08157563209533691}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2017.7950514","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2017.7950514","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W114255859","https://openalex.org/W845365781","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1910657905","https://openalex.org/W2028140761","https://openalex.org/W2035427371","https://openalex.org/W2037956299","https://openalex.org/W2076013257","https://openalex.org/W2083652081","https://openalex.org/W2113790176","https://openalex.org/W2143516773","https://openalex.org/W2155893237","https://openalex.org/W2418385737","https://openalex.org/W2513326255","https://openalex.org/W2526842628","https://openalex.org/W3098547059","https://openalex.org/W4306369316","https://openalex.org/W6639824700"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3185137224","https://openalex.org/W3194017912"],"abstract_inverted_index":{"Accurate":[0],"skin":[1,15,143],"lesion":[2,16,73,105,117],"segmentation":[3,17,144],"is":[4,18,121],"an":[5],"important":[6],"yet":[7],"challenging":[8],"problem":[9],"for":[10,71,98],"medical":[11],"image":[12],"analysis.":[13],"The":[14],"subject":[19],"to":[20,54,91],"variety":[21],"of":[22,37,57,60,84],"challenges":[23],"such":[24,108],"as":[25,109],"the":[26,34,72,85,99,110,116,124],"significant":[27],"pattern":[28],"and":[29,69,80],"colour":[30],"diversity":[31],"found":[32],"within":[33],"lesions,":[35],"presence":[36],"various":[38],"artifacts,":[39],"etc.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,77],"present":[45],"two":[46],"fully":[47],"convolutional":[48],"networks":[49],"with":[50,66,141],"several":[51],"side":[52],"outputs":[53],"take":[55],"advantage":[56],"discriminative":[58],"capability":[59],"features":[61],"learned":[62],"at":[63],"intermediate":[64],"layers":[65],"varying":[67],"resolutions":[68],"scales":[70],"segmentation.":[74,118],"More":[75],"specifically,":[76],"integrate":[78],"fine":[79,104],"coarse":[81],"prediction":[82],"scores":[83],"side-layers":[86],"which":[87,113,134],"allows":[88],"our":[89,136],"framework":[90],"not":[92],"only":[93],"output":[94],"accurate":[95],"probability":[96],"map":[97],"lesion,":[100],"but":[101],"also":[102],"extract":[103],"boundary":[106],"details":[107],"fuzzy":[111],"border,":[112],"further":[114],"improves":[115],"Quantitative":[119],"evaluation":[120],"performed":[122],"on":[123,128],"2016":[125],"International":[126],"Symposium":[127],"Biomedical":[129],"Imaging":[130],"(ISBI":[131],"2016)":[132],"dataset,":[133],"shows":[135],"proposed":[137],"approach":[138],"compares":[139],"favorably":[140],"state-of-the-art":[142],"methods.":[145]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}