{"id":"https://openalex.org/W7124146882","doi":"https://doi.org/10.1145/3777577.3777662","title":"VM-UNet-SD: Multi-Scale Self-Distillation for Skin Lesion SegmentationA Multi-Scale Self-Distillation Framework for Skin Lesion SegmentationVM-UNET-SD: Self-Distillation Segmentation","display_name":"VM-UNet-SD: Multi-Scale Self-Distillation for Skin Lesion SegmentationA Multi-Scale Self-Distillation Framework for Skin Lesion SegmentationVM-UNET-SD: Self-Distillation Segmentation","publication_year":2025,"publication_date":"2025-10-24","ids":{"openalex":"https://openalex.org/W7124146882","doi":"https://doi.org/10.1145/3777577.3777662"},"language":null,"primary_location":{"id":"doi:10.1145/3777577.3777662","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777662","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3777577.3777662","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119014409","display_name":"Feidan Kou","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feidan Kou","raw_affiliation_strings":["Information Materials and Intelligent Sensing Laboratory of Anhui Province, Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Anhui University, Hefei, Anhui, China"],"affiliations":[{"raw_affiliation_string":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Anhui University, Hefei, Anhui, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123010339","display_name":"Jun Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wu","raw_affiliation_strings":["Second Information Materials and Intelligent Sensing Laboratory of Anhui Province, Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Anhui University, Hefei, Anhui, China"],"affiliations":[{"raw_affiliation_string":"Second Information Materials and Intelligent Sensing Laboratory of Anhui Province, Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Anhui University, Hefei, Anhui, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101055618","display_name":"Haoran Feng","orcid":"https://orcid.org/0000-0003-0335-3242"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Feng","raw_affiliation_strings":["Shanghai Jiaotong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiaotong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5119014409"],"corresponding_institution_ids":["https://openalex.org/I143868143"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71408271,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"515","last_page":"520"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.8952000141143799,"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":0.8952000141143799,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.016899999231100082,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.011699999682605267,"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/segmentation","display_name":"Segmentation","score":0.6470000147819519},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5300999879837036},{"id":"https://openalex.org/keywords/skin-lesion","display_name":"Skin lesion","score":0.5127999782562256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45719999074935913},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.447299987077713},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.40959998965263367},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39899998903274536},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.39629998803138733},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3887999951839447}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.691100001335144},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6470000147819519},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6450999975204468},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5300999879837036},{"id":"https://openalex.org/C2988168687","wikidata":"https://www.wikidata.org/wiki/Q949302","display_name":"Skin lesion","level":2,"score":0.5127999782562256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45719999074935913},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.447299987077713},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.39629998803138733},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.3707999885082245},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35190001130104065},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3777577.3777662","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777662","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3777577.3777662","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777662","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2691010290","https://openalex.org/W2954996726","https://openalex.org/W3035104321","https://openalex.org/W4310358279","https://openalex.org/W4386438388","https://openalex.org/W4396980138","https://openalex.org/W4414243662"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"state":[3],"space":[4],"models":[5,174],"(SSMs)":[6],"have":[7],"demonstrated":[8],"tremendous":[9],"potential":[10],"in":[11,38,52,72,130,175],"long-sequence":[12],"modeling":[13],"tasks.":[14],"As":[15],"the":[16,30,33,63,81,103,106,110,131,139,146,151,166,190,193,202],"first":[17],"medical":[18,207],"image":[19,208],"segmentation":[20,159,209],"model":[21,86],"purely":[22],"based":[23],"on":[24,80,150],"SSMs,":[25],"VM-UNet":[26,168],"has":[27],"successfully":[28],"verified":[29],"applicability":[31],"of":[32,54,83,102,115,141,172,177,192,205],"Visual":[34],"Mamba":[35],"(VSS)":[36],"module":[37,67],"this":[39,75,85],"field.":[40],"However,":[41],"as":[42],"a":[43,125,170],"baseline":[44],"model,":[45],"there":[46],"is":[47],"still":[48],"room":[49],"for":[50,201],"improvement":[51],"terms":[53,176],"multi-scale":[55,120],"feature":[56,59],"fusion":[57],"and":[58,68,112,144,154,169,182],"learning":[60,140],"efficiency.Inspired":[61],"by":[62],"semantic":[64,91,111],"detail":[65,92],"injection":[66,93],"progressive":[69,126],"self-distillation":[70,121],"network":[71],"U-Net":[73],"v2,":[74],"paper":[76],"proposes":[77],"VM-UNet-SD.":[78],"Based":[79],"architecture":[82],"VM-UNet,":[84],"introduces":[87],"two":[88],"core":[89],"improvements:1)The":[90],"module:":[94],"It":[95,123],"adaptively":[96],"fuses":[97],"features":[98,116,135,143],"from":[99],"different":[100],"levels":[101],"encoder":[104],"through":[105],"Hadamard":[107],"product,":[108],"enriching":[109],"detailed":[113],"information":[114],"at":[117],"each":[118],"scale;2)The":[119],"strategy:":[122],"establishes":[124],"knowledge":[127],"distillation":[128],"mechanism":[129],"decoder,":[132],"enabling":[133],"deep-layer":[134],"to":[136],"gradually":[137],"guide":[138],"shallow-layer":[142],"enhance":[145],"model's":[147],"representation":[148],"capability.Experiments":[149],"ISIC":[152,155],"2017":[153],"2018":[156],"skin":[157],"lesion":[158],"datasets":[160],"show":[161],"that":[162],"VM-UNet-SD":[163],"significantly":[164],"outperforms":[165],"original":[167],"series":[171],"advanced":[173],"Dice":[178],"Similarity":[179],"Coefficient":[180],"(DSC)":[181],"Intersection":[183],"over":[184],"Union":[185],"(IoU)":[186],"metrics,":[187],"which":[188],"proves":[189],"effectiveness":[191],"proposed":[194],"modules.":[195],"This":[196],"study":[197],"provides":[198],"new":[199],"insights":[200],"performance":[203],"optimization":[204],"SSM-based":[206],"models.":[210]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-01-15T00:00:00"}
