{"id":"https://openalex.org/W4390970048","doi":"https://doi.org/10.1109/bibm58861.2023.10385742","title":"MFF-Net: Multiscale Feature Fusion Network for Skin Lesion Segmentation","display_name":"MFF-Net: Multiscale Feature Fusion Network for Skin Lesion Segmentation","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390970048","doi":"https://doi.org/10.1109/bibm58861.2023.10385742"},"language":"en","primary_location":{"id":"doi:10.1109/bibm58861.2023.10385742","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5113098607","display_name":"Shujun Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shujun Ren","raw_affiliation_strings":["Shandong University of Science and Technology,College of Computer Science and Engineering,Qingdao,China","College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology,College of Computer Science and Engineering,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]},{"raw_affiliation_string":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086147576","display_name":"Yanjun Peng","orcid":"https://orcid.org/0000-0002-8444-0622"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjun Peng","raw_affiliation_strings":["Shandong University of Science and Technology,College of Computer Science and Engineering,Qingdao,China","College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology,College of Computer Science and Engineering,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]},{"raw_affiliation_string":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5113098607"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":null,"apc_paid":null,"fwci":0.2384,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71157844,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3473","last_page":"3479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":1.0,"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":1.0,"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":0.9918000102043152,"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/T11569","display_name":"Optical Coherence Tomography Applications","score":0.9611999988555908,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7114060521125793},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7049403190612793},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6692938804626465},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6645141243934631},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5964756011962891},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.49141204357147217},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4827958345413208},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48116669058799744},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4181334376335144},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41459307074546814}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7114060521125793},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7049403190612793},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6692938804626465},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6645141243934631},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5964756011962891},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.49141204357147217},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4827958345413208},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48116669058799744},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4181334376335144},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41459307074546814},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm58861.2023.10385742","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2061253660","https://openalex.org/W2787091153","https://openalex.org/W2794825826","https://openalex.org/W2798122215","https://openalex.org/W2921406441","https://openalex.org/W2932083555","https://openalex.org/W2963604034","https://openalex.org/W2987039128","https://openalex.org/W3013198566","https://openalex.org/W3088257970","https://openalex.org/W3093038272","https://openalex.org/W3096812112","https://openalex.org/W3102785203","https://openalex.org/W3200640732","https://openalex.org/W3205816419","https://openalex.org/W4200015473","https://openalex.org/W4220888491","https://openalex.org/W4295934721","https://openalex.org/W4295940381","https://openalex.org/W4300001742","https://openalex.org/W6693768309","https://openalex.org/W6750469568","https://openalex.org/W6761152059","https://openalex.org/W6784577829"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W2611989081","https://openalex.org/W3155418658","https://openalex.org/W4243199227"],"abstract_inverted_index":{"Melanoma":[0],"is":[1,63,106],"a":[2,57,109,113],"particularly":[3],"aggressive":[4],"cancer;":[5],"however,":[6],"early":[7,29],"detection":[8],"and":[9,41,44,52,137,169,178,188],"treatments":[10],"can":[11],"increase":[12],"patient":[13],"survival":[14],"rates.":[15],"Deep":[16],"learning":[17],"techniques":[18],"have":[19],"practical":[20],"applications":[21],"in":[22,27,54,65,119,132,203],"segmentation":[23,136,163],"to":[24,122,127,155],"assist":[25],"physicians":[26],"making":[28],"melanoma":[30],"diagnoses.":[31],"For":[32],"the":[33,78,92,97,103,120,129,138,145,148,189,192,197],"problems":[34],"of":[35,81,96,102,140,147,199,205],"lesion":[36,50,82,134,162],"regions":[37,51],"with":[38,191],"different":[39],"shapes":[40],"sizes,":[42],"discontinuous":[43],"blurred":[45],"boundaries,":[46],"high":[47],"similarity":[48],"between":[49],"background":[53],"dermoscopic":[55],"images,":[56],"multi-scale":[58],"feature":[59,86,93,104,115],"fusion":[60,87,116],"network":[61,69],"(MFF-Net)":[62],"proposed":[64,149,201],"this":[66],"paper.":[67],"The":[68],"contains":[70],"an":[71],"edge":[72,79],"enhancement":[73],"module":[74,88,117],"(EEM)":[75],"for":[76,90],"enhancing":[77,91],"features":[80],"regions.":[83],"A":[84],"multi-branch":[85],"(MBF)":[89],"extraction":[94],"capability":[95],"network.":[98],"More":[99],"local":[100],"continuity":[101],"map":[105],"obtained":[107],"by":[108],"hybrid":[110],"encoder.":[111],"Using":[112],"global":[114],"(GFF)":[118],"decoder":[121],"learn":[123],"more":[124],"contextual":[125],"information":[126],"alleviate":[128],"hair":[130],"interference":[131],"skin":[133,161],"image":[135],"problem":[139],"low":[141],"contrast.":[142],"To":[143],"validate":[144],"effectiveness":[146],"network,":[150],"we":[151],"conducted":[152],"extensive":[153],"experiments":[154],"evaluate":[156],"it":[157],"on":[158,174,182,209],"three":[159],"public":[160],"datasets":[164],"(ISIC2018,":[165],"PH<sup":[166,183],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[167,184],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>,":[168],"HAM10000),":[170],"Dice":[171],"reached":[172,180],"93.17%":[173],"ISIC2018":[175],"data":[176,186],"set,":[177],"IoU":[179],"93.88%":[181],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[185],"set":[187],"comparison":[190],"latest":[193],"methods":[194],"also":[195],"validates":[196],"superiority":[198],"our":[200],"MFF-Net":[202],"terms":[204],"accuracy.":[206],"Our":[207],"code":[208],"https://github.com/gih23/MFF-Net.":[210]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}