{"id":"https://openalex.org/W7147726524","doi":"https://doi.org/10.1109/cnml68938.2026.11453214","title":"Rethinking the Frequency Domain Information in Skin Lesion Segmentation","display_name":"Rethinking the Frequency Domain Information in Skin Lesion Segmentation","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147726524","doi":"https://doi.org/10.1109/cnml68938.2026.11453214"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11453214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453214","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5101281582","display_name":"Yijiao Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I161346416","display_name":"Jimei University","ror":"https://ror.org/03hknyb50","country_code":"CN","type":"education","lineage":["https://openalex.org/I161346416"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yijiao Lin","raw_affiliation_strings":["Jimei University,School of Ocean Information Engineering,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Jimei University,School of Ocean Information Engineering,Xiamen,China","institution_ids":["https://openalex.org/I161346416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107269331","display_name":"Duxin Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I161346416","display_name":"Jimei University","ror":"https://ror.org/03hknyb50","country_code":"CN","type":"education","lineage":["https://openalex.org/I161346416"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Duxin Zhu","raw_affiliation_strings":["Jimei University,College of Computer Engineering,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Jimei University,College of Computer Engineering,Xiamen,China","institution_ids":["https://openalex.org/I161346416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101281582"],"corresponding_institution_ids":["https://openalex.org/I161346416"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.89336404,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"668","last_page":"671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9873999953269958,"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":0.9873999953269958,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0013000000035390258,"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"}},{"id":"https://openalex.org/T11306","display_name":"Nonmelanoma Skin Cancer Studies","score":0.0013000000035390258,"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/frequency-domain","display_name":"Frequency domain","score":0.6577000021934509},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6134999990463257},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5493000149726868},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5365999937057495},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.46889999508857727},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4293999969959259},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33820000290870667},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32350000739097595}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6776999831199646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682000160217285},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.6577000021934509},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6134999990463257},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5493000149726868},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5447999835014343},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5365999937057495},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4293999969959259},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.31690001487731934},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3086000084877014},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2639000117778778},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C100921725","wikidata":"https://www.wikidata.org/wiki/Q1650811","display_name":"Spatial frequency","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11453214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453214","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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":11,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2884436604","https://openalex.org/W2928165649","https://openalex.org/W2963946669","https://openalex.org/W3081752372","https://openalex.org/W3092344722","https://openalex.org/W3175515048","https://openalex.org/W4281294707","https://openalex.org/W4399575526","https://openalex.org/W4413157541","https://openalex.org/W7131112959"],"related_works":[],"abstract_inverted_index":{"Transformer-based":[0],"models":[1],"have":[2,21],"demonstrated":[3],"success":[4],"in":[5,23,28,68,84,127],"skin":[6],"lesion":[7,69],"segmentation,":[8],"effectively":[9,143],"handling":[10],"challenges":[11],"like":[12],"fuzzy":[13],"boundaries":[14],"and":[15,60,77,91],"color":[16],"variations.":[17],"However,":[18],"they":[19],"may":[20],"limitations":[22],"addressing":[24],"inherent":[25],"noise":[26,43],"issues":[27],"dermoscopic":[29],"images.":[30],"Notably,":[31],"we":[32,54,72,107],"observed":[33],"that":[34],"the":[35,62,65,74,85,98,109,123,128,139,148,162],"loss":[36],"of":[37,64,100,171],"high-frequency":[38],"components":[39],"due":[40],"to":[41],"image":[42],"can":[44],"significantly":[45],"impact":[46],"model":[47],"performance.":[48],"Drawing":[49],"inspiration":[50],"from":[51],"this":[52],"observation,":[53],"propose":[55,73],"a":[56,168],"novel":[57,140],"heterogeneous":[58],"network":[59],"reevaluate":[61],"significance":[63],"frequency":[66,86,129],"domain":[67],"representation.":[70],"Specifically,":[71],"Frequency-domain":[75],"Transfer":[76],"Contact":[78],"(FTC)":[79],"module":[80,142],"for":[81],"information":[82,134],"exchange":[83],"domain.":[87],"By":[88],"employing":[89],"convolution":[90],"quadratic":[92],"Fourier":[93,110,118],"transforms,":[94],"semantic":[95],"features":[96],"guide":[97],"reconstruction":[99],"fine":[101],"details":[102],"at":[103],"different":[104],"scales.":[105],"Additionally,":[106],"introduce":[108],"Transform-based":[111],"Supervision":[112],"(FTS)":[113],"technique.":[114],"FTS":[115,150],"utilizes":[116],"secondary":[117],"transforms":[119],"on":[120],"labels,":[121],"enhancing":[122],"network\u2019s":[124],"learning":[125],"guidance":[126],"domain,":[130],"where":[131],"crucial":[132],"edge":[133],"resides.":[135],"In":[136],"our":[137,165],"experiments,":[138],"FTC":[141],"enhances":[144],"detailed":[145],"information,":[146],"while":[147],"plug-and-play":[149],"supervision":[151],"method":[152,166],"demonstrates":[153],"immediate":[154],"improvements":[155],"when":[156],"integrated":[157],"into":[158],"existing":[159],"networks.":[160],"On":[161],"ISIC2017":[163],"dataset,":[164],"achieves":[167],"Dice":[169],"score":[170],"0.9050,":[172],"outperforming":[173],"several":[174],"strong":[175],"baselines.":[176]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}