{"id":"https://openalex.org/W4408354242","doi":"https://doi.org/10.1109/icassp49660.2025.10890573","title":"Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine Images","display_name":"Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine Images","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354242","doi":"https://doi.org/10.1109/icassp49660.2025.10890573"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10890573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890573","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5101735221","display_name":"Dong Zhang","orcid":"https://orcid.org/0000-0002-7494-7817"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Zhang","raw_affiliation_strings":["Tianjin University,College of Intelligence and Computing,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University,College of Intelligence and Computing,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084555916","display_name":"Meijun Sun","orcid":"https://orcid.org/0000-0002-8691-8677"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meijun Sun","raw_affiliation_strings":["Tianjin University,College of Intelligence and Computing,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University,College of Intelligence and Computing,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79134628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8841000199317932,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8841000199317932,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.8133000135421753,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.7936000227928162,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7926836013793945},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6943959593772888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.692806601524353},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6633367538452148},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5678006410598755},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5085576772689819},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48246079683303833},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47301676869392395},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4623618721961975},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4101802706718445},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1296767294406891}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7926836013793945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6943959593772888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.692806601524353},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6633367538452148},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5678006410598755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5085576772689819},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48246079683303833},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47301676869392395},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4623618721961975},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4101802706718445},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1296767294406891},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10890573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890573","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2890325102","https://openalex.org/W2928133111","https://openalex.org/W2991616716","https://openalex.org/W3011934240","https://openalex.org/W3016888800","https://openalex.org/W3034050230","https://openalex.org/W3039548502","https://openalex.org/W3045295565","https://openalex.org/W3097571420","https://openalex.org/W3106904274","https://openalex.org/W3203480968","https://openalex.org/W3208487514","https://openalex.org/W4200019180","https://openalex.org/W4293370522","https://openalex.org/W4312672320","https://openalex.org/W4312836739","https://openalex.org/W4313153210","https://openalex.org/W4387831727"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2070598848","https://openalex.org/W2019190440","https://openalex.org/W3034864990","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Hyperspectral":[0],"images":[1,68,113],"had":[2,175],"made":[3],"many":[4],"applications":[5],"in":[6,28,178],"the":[7,43,49,53,58,99,104,125,157,162,172],"medical":[8,66],"field":[9,45],"with":[10,20,83],"their":[11],"rich":[12],"spectral":[13,34],"information.":[14],"However,":[15],"there":[16],"were":[17],"currently":[18],"problems":[19],"feature":[21,31,109,130],"extraction":[22],"based":[23],"on":[24,71,98,161],"hyperspectral":[25,63,67,94,112,163],"images,":[26,64],"especially":[27],"extracting":[29],"contextual":[30],"information":[32],"from":[33],"bands,":[35],"and":[36,46,132,147,153,183,185],"a":[37,79,115],"single":[38,179],"convolutional":[39,191],"kernel":[40],"may":[41],"restrict":[42],"receptive":[44],"inadequately":[47],"capture":[48],"sequential":[50],"properties":[51],"of":[52,62,111,156,165,181],"data.":[54],"Meanwhile,":[55],"due":[56],"to":[57,88,140],"large":[59],"data":[60],"volume":[61],"current":[65],"focus":[69],"more":[70],"individual":[72],"segmentation":[73,146,152,182],"or":[74],"classification.":[75,148],"This":[76],"paper":[77],"proposed":[78,173],"3D":[80,100,120],"swin":[81,101],"transformer":[82,102,121],"multi-task":[84,190],"joint":[85],"learning":[86,110],"framework,":[87],"simultaneously":[89],"learn":[90],"multiple":[91],"tasks":[92,143,155,180],"for":[93,128,137],"tongue":[95,158,166],"images.":[96,167],"Based":[97],"model,":[103],"framework":[105,127],"regards":[106],"cross-band":[107],"context":[108],"as":[114,124,145],"sequence-to-sequence":[116],"prediction":[117,138],"process.":[118],"The":[119,168],"encoder":[122],"used":[123],"basic":[126],"shared":[129],"extraction,":[131],"set":[133],"up":[134],"corresponding":[135],"decoders":[136],"according":[139],"different":[141],"visual":[142],"such":[144],"We":[149],"conducted":[150],"simultaneous":[151],"classification":[154],"coating":[159],"region":[160],"image":[164],"results":[169,177],"showed":[170],"that":[171],"model":[174],"good":[176],"classification,":[184],"performed":[186],"better":[187],"than":[188],"other":[189],"neural":[192],"network":[193],"models.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
