{"id":"https://openalex.org/W7163207635","doi":"https://doi.org/10.3390/computers15060359","title":"Hybrid Transformer Model with Augmentation for Kidney Tumor Segmentation","display_name":"Hybrid Transformer Model with Augmentation for Kidney Tumor Segmentation","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163207635","doi":"https://doi.org/10.3390/computers15060359"},"language":"en","primary_location":{"id":"doi:10.3390/computers15060359","doi":"https://doi.org/10.3390/computers15060359","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers15060359","pdf_url":null,"source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/computers15060359","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137704011","display_name":"Rajagopal Kumaraswamy","orcid":"https://orcid.org/0009-0004-4230-4157","observed_orcids":["https://orcid.org/0009-0004-4230-4157"]},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajagopal Kumaraswamy","raw_affiliation_strings":["Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105,  India"],"raw_orcid":"https://orcid.org/0009-0004-4230-4157","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105,  India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091281388","display_name":"V Sheeja Kumari","orcid":"https://orcid.org/0000-0001-5505-0345","observed_orcids":["https://orcid.org/0000-0001-5505-0345"]},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"V. Sheeja Kumari","raw_affiliation_strings":["Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105,  India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105,  India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115065062","display_name":"N.Muthuvairavan Pillai","orcid":"https://orcid.org/0000-0002-6310-2763","observed_orcids":["https://orcid.org/0000-0002-6310-2763"]},"institutions":[{"id":"https://openalex.org/I4387154816","display_name":"R.M.D. Engineering College","ror":"https://ror.org/03gjktv92","country_code":"IN","type":"education","lineage":["https://openalex.org/I4387154816"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"N. Muthuvairavan Pillai","raw_affiliation_strings":["Department of Computer Science and Business Systems, R.M.D Engineering College, Gummidipoondi 601206, India"],"raw_orcid":"https://orcid.org/0000-0002-6310-2763","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Business Systems, R.M.D Engineering College, Gummidipoondi 601206, India","institution_ids":["https://openalex.org/I4387154816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099252763","display_name":"Dr R. H. Aswathy","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. H. Aswathy","raw_affiliation_strings":["Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105,  India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105,  India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137654524","display_name":"Vijayalakshmi Ramakumar","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405271589","display_name":"Sri Sairam Engineering College","ror":"https://ror.org/02pjbf956","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405271589"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vijayalakshmi Ramakumar","raw_affiliation_strings":["Department of Artificial Intelligence and Data Science, Sri Sairam College of Engineering, Chennai 600044, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence and Data Science, Sri Sairam College of Engineering, Chennai 600044, India","institution_ids":["https://openalex.org/I4405271589"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041959931","display_name":"Indra Neel Pulidindi","orcid":"https://orcid.org/0000-0002-3281-9649","observed_orcids":["https://orcid.org/0000-0002-3281-9649"]},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Indra Neel Pulidindi","raw_affiliation_strings":["Department of Ear, Nose and Throat, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences Deemed University, Chennai 602105, India"],"raw_orcid":"https://orcid.org/0000-0002-3281-9649","affiliations":[{"raw_affiliation_string":"Department of Ear, Nose and Throat, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences Deemed University, Chennai 602105, India","institution_ids":["https://openalex.org/I85461943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1800,"currency":"CHF","value_usd":2172},"apc_paid":{"value":1800,"currency":"CHF","value_usd":2172},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"6","first_page":"359","last_page":"359"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9945999979972839,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9945999979972839,"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":0.0020000000949949026,"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/T10862","display_name":"AI in cancer detection","score":0.0017000000225380063,"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/kidney-tumor-segmentation","display_name":"kidney tumor segmentation","score":1.0},{"id":"https://openalex.org/keywords/swin-transformer","display_name":"Swin Transformer","score":0.7760000228881836},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.8029999732971191},{"id":"https://openalex.org/keywords/medical-image-segmentation","display_name":"medical image segmentation","score":0.8600000143051147},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.8399999737739563},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9620000123977661},{"id":"https://openalex.org/keywords/multiscale-feature-extraction","display_name":"multi-scale feature extraction","score":0.8700000047683716},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"computer-aided diagnosis","score":0.9850000143051147}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7361999750137329},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6942999958992004},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.6710000038146973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6572999954223633},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6251999735832214},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5378999710083008},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5300999879837036},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.46639999747276306},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3684999942779541},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2797999978065491},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2754000127315521},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/computers15060359","doi":"https://doi.org/10.3390/computers15060359","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers15060359","pdf_url":null,"source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:319304d4230243f08c23003542d12731","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/319304d4230243f08c23003542d12731","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computers, Vol 15, Iss 6, p 359 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/computers15060359","doi":"https://doi.org/10.3390/computers15060359","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers15060359","pdf_url":null,"source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5683000087738037,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2993476821","https://openalex.org/W3119856975","https://openalex.org/W4200274794","https://openalex.org/W4220739817","https://openalex.org/W4220985359","https://openalex.org/W4312737431","https://openalex.org/W4390824004","https://openalex.org/W4392732434","https://openalex.org/W4392982038","https://openalex.org/W4394862823","https://openalex.org/W4396621542","https://openalex.org/W4399225253","https://openalex.org/W4400116344","https://openalex.org/W4400252098","https://openalex.org/W4400833236","https://openalex.org/W4401210389","https://openalex.org/W4401335962","https://openalex.org/W4401577362","https://openalex.org/W4401798874","https://openalex.org/W4402498603","https://openalex.org/W4403772155","https://openalex.org/W4405160089","https://openalex.org/W4406021351","https://openalex.org/W4406125343","https://openalex.org/W4406706840","https://openalex.org/W4408951643","https://openalex.org/W4409176709","https://openalex.org/W4412073578","https://openalex.org/W4412447166","https://openalex.org/W4415821899"],"related_works":["https://openalex.org/W4392509390","https://openalex.org/W4389302056","https://openalex.org/W4402916240","https://openalex.org/W4220739817","https://openalex.org/W4392500383","https://openalex.org/W4402498603","https://openalex.org/W7221563374","https://openalex.org/W7213367698","https://openalex.org/W7160430308","https://openalex.org/W7210288241"],"abstract_inverted_index":{"Precise":[0],"segmentation":[1,45,249],"of":[2,33,201,226,230,234,241,279,295],"kidney":[3,247],"tumors":[4],"in":[5,40,61,83,115,120,199,267],"medical":[6,284,305],"images":[7],"is":[8,66,260],"crucial":[9],"for":[10,283,292],"diagnosis,":[11],"treatment":[12],"planning,":[13],"and":[14,35,81,135,164,190,214,236,245,254,263,299],"prognosis":[15],"assessment.":[16],"In":[17],"this":[18],"work,":[19],"we":[20,107],"present":[21],"a":[22,62,181,221,232],"newly":[23],"proposed":[24,218],"hybrid":[25,99,281],"deep":[26],"learning":[27,184],"model":[28,138,219],"that":[29,169],"combines":[30],"the":[31,36,44,56,111,116,137,140,177,191,202,257,277,293],"merits":[32],"U-Net":[34,48,109,189],"Swin":[37,75,112,192],"Transformer":[38,76,113,193],"architectures":[39,198],"order":[41,121],"to":[42,55,69,103,122,160],"enhance":[43],"performance.":[46],"Although":[47],"has":[49],"great":[50],"spatial":[51],"localization":[52],"ability":[53],"thanks":[54],"encoder\u2013decoder":[57],"structure,":[58],"which":[59],"works":[60],"hierarchical":[63],"way,":[64],"it":[65,170],"still":[67],"difficult":[68],"capture":[70],"global":[71],"context":[72],"well.":[73],"The":[74,217],"instead":[77],"captures":[78],"long-range":[79],"dependencies":[80],"assists":[82],"local":[84],"detail":[85],"extraction,":[86],"while":[87,129],"attention":[88],"pooling":[89],"might":[90],"also":[91,195],"smear":[92],"fine":[93],"boundary":[94,165],"details.":[95],"This":[96],"motivates":[97],"our":[98],"integration.":[100],"To":[101],"attempt":[102],"resolve":[104],"these":[105],"issues,":[106],"extend":[108],"with":[110,150,176,270,302],"blocks":[114],"backbone":[117],"encoder":[118],"path":[119],"efficiently":[123],"perform":[124],"multi-scale":[125],"semantic":[126],"feature":[127],"extraction":[128],"preserving":[130],"structural":[131],"consistency.":[132],"We":[133],"trained":[134],"cross-validated":[136],"on":[139,186],"publicly":[141],"available":[142],"Kidney":[143],"Tumor":[144],"Segmentation":[145],"Challenge":[146],"2021":[147],"(KiTS21)":[148],"dataset":[149],"extensive":[151],"data":[152],"augmentation":[153],"as":[154,156],"well":[155],"custom":[157],"loss":[158],"functions":[159],"address":[161],"class":[162],"imbalance":[163],"obscureness.":[166],"Experiments":[167],"demonstrated":[168],"achieved":[171,220],"better":[172],"performance":[173],"when":[174],"compared":[175],"solo":[178],"models,":[179],"seeking":[180],"similar":[182],"multi-task":[183],"objective":[185],"not":[187],"only":[188],"but":[194],"other":[196,303],"baseline":[197],"terms":[200],"average":[203],"Dice":[204,222],"similarity":[205,223],"coefficient":[206,224],"(average":[207],"DSC),":[208],"intersection":[209],"over":[210],"union":[211],"score":[212],"(IoU)":[213],"Hausdorff":[215],"distance.":[216],"(DSC)":[225],"0.91,":[227],"an":[228,237],"IoU":[229],"0.87,":[231],"PR-AUC":[233],"0.89,":[235],"overall":[238],"voxel-wise":[239],"accuracy":[240],"98%,":[242],"demonstrating":[243],"robust":[244,262],"precise":[246],"tumor":[248,252],"across":[250],"varying":[251],"sizes":[253],"shapes.":[255],"Moreover,":[256],"integrated":[258],"solution":[259],"more":[261],"generalizes":[264],"better,":[265],"particularly":[266],"challenging":[268],"cases":[269],"diverse":[271],"anatomical":[272],"variations.":[273],"These":[274],"findings":[275],"demonstrate":[276],"power":[278],"Transformer-based":[280],"models":[282],"image":[285],"segmentation.":[286],"Our":[287],"results":[288],"have":[289],"positive":[290],"implications":[291],"design":[294],"computer-aided":[296],"diagnostic":[297],"systems":[298],"their":[300],"association":[301],"prevalent":[304],"imaging":[306],"tasks":[307],"besides":[308],"organ-specific":[309],"or":[310],"pathology-focused":[311],"tasks.":[312]},"counts_by_year":[],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2026-06-03T00:00:00"}
