{"id":"https://openalex.org/W7130341846","doi":"https://doi.org/10.48550/arxiv.2602.15067","title":"Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis","display_name":"Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis","publication_year":2026,"publication_date":"2026-02-14","ids":{"openalex":"https://openalex.org/W7130341846","doi":"https://doi.org/10.48550/arxiv.2602.15067"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15067","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.15067","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126358585","display_name":"Rut Pate","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pate, Rut","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019021656","display_name":"Snehal Rajput","orcid":"https://orcid.org/0000-0001-8240-3740"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajput, Snehal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071626652","display_name":"Mehul S. Raval","orcid":"https://orcid.org/0000-0002-3895-1448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raval, Mehul S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024122307","display_name":"Rupal Kapdi","orcid":"https://orcid.org/0000-0003-1995-4149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kapdi, Rupal A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082017465","display_name":"Mohendra Roy","orcid":"https://orcid.org/0000-0001-5815-3294"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy, Mohendra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.659500002861023,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.659500002861023,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.2870999872684479,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.012000000104308128,"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":0.6711000204086304},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.6134999990463257},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.59170001745224},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5788999795913696},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5271000266075134},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4311999976634979},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4009000062942505}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7139000296592712},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6711000204086304},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.6134999990463257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6003000140190125},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5788999795913696},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5271000266075134},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3799999952316284},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C44083865","wikidata":"https://www.wikidata.org/wiki/Q3853443","display_name":"Mean reciprocal rank","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15067","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15067","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5715458989143372,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gliomas,":[0],"among":[1],"the":[2,86,97,145],"most":[3],"common":[4],"primary":[5],"brain":[6,40],"tumors,":[7],"vary":[8],"widely":[9],"in":[10,65],"aggressiveness,":[11],"prognosis,":[12],"and":[13,21,49,56,135],"histology,":[14],"making":[15],"treatment":[16,67],"challenging":[17],"due":[18],"to":[19,93,113],"complex":[20],"time-intensive":[22],"surgical":[23],"interventions.":[24],"This":[25,121],"study":[26],"presents":[27],"an":[28,116,124],"Attention-Gated":[29],"Recurrent":[30],"Residual":[31],"U-Net":[32],"(R2U-Net)":[33],"based":[34],"Triplanar":[35],"(2.5D)":[36],"model":[37,45,105],"for":[38,80,106],"improved":[39],"tumor":[41],"segmentation.":[42],"The":[43,69],"proposed":[44,70],"enhances":[46],"feature":[47],"representation":[48],"segmentation":[50,84],"accuracy":[51,125],"by":[52],"integrating":[53],"residual,":[54],"recurrent,":[55],"triplanar":[57,98],"architectures":[58],"while":[59],"maintaining":[60],"computational":[61],"efficiency,":[62],"potentially":[63],"aiding":[64],"better":[66],"planning.":[68],"method":[71],"achieves":[72,123],"a":[73,128,136],"Dice":[74],"Similarity":[75],"Score":[76],"(DSC)":[77],"of":[78,126,133,142],"0.900":[79],"Whole":[81],"Tumor":[82],"(WT)":[83],"on":[85,144],"BraTS2021":[87],"validation":[88],"set,":[89],"demonstrating":[90],"performance":[91],"comparable":[92],"leading":[94],"models.":[95],"Additionally,":[96],"network":[99],"extracts":[100],"64":[101],"features":[102],"per":[103],"planar":[104],"survival":[107],"days":[108],"prediction,":[109],"which":[110],"are":[111],"reduced":[112],"28":[114],"using":[115],"Artificial":[117],"Neural":[118],"Network":[119],"(ANN).":[120],"approach":[122],"45.71%,":[127],"Mean":[129],"Squared":[130],"Error":[131],"(MSE)":[132],"108,318.128,":[134],"Spearman":[137],"Rank":[138],"Correlation":[139],"Coefficient":[140],"(SRC)":[141],"0.338":[143],"test":[146],"dataset.":[147]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-19T00:00:00"}