{"id":"https://openalex.org/W2953239601","doi":"https://doi.org/10.1109/ncc.2019.8732196","title":"Brain Tumor Segmentation Using Discriminator Loss","display_name":"Brain Tumor Segmentation Using Discriminator Loss","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2953239601","doi":"https://doi.org/10.1109/ncc.2019.8732196","mag":"2953239601"},"language":"en","primary_location":{"id":"doi:10.1109/ncc.2019.8732196","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2019.8732196","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 National Conference on Communications (NCC)","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/A5034487572","display_name":"Joydeep Das","orcid":"https://orcid.org/0000-0002-6317-0029"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Joydeep Das","raw_affiliation_strings":["Department of Electronics and Communication Engineering, IIT Roorkee"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, IIT Roorkee","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110135368","display_name":"Rashmin Patel","orcid":null},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rashmin Patel","raw_affiliation_strings":["Department of Electronics and Communication Engineering, IIT Roorkee"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, IIT Roorkee","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059884867","display_name":"Vinod Pankajakshan","orcid":"https://orcid.org/0000-0003-4525-5288"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vinod Pankajakshan","raw_affiliation_strings":["Department of Electronics and Communication Engineering, IIT Roorkee"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, IIT Roorkee","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034487572"],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":null,"apc_paid":null,"fwci":0.3219,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6282512,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"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":1.0,"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":1.0,"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":0.9998999834060669,"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":0.9979000091552734,"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/discriminator","display_name":"Discriminator","score":0.9306991100311279},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7916684150695801},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6687343120574951},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6419051885604858},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5968772172927856},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5919464826583862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.509746253490448},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5047034025192261},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46246832609176636},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.348688006401062},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.0851094126701355}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.9306991100311279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7916684150695801},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6687343120574951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6419051885604858},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5968772172927856},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5919464826583862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.509746253490448},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5047034025192261},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46246832609176636},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.348688006401062},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0851094126701355},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc.2019.8732196","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2019.8732196","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 National Conference on Communications (NCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1641498739","https://openalex.org/W1884191083","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2099471712","https://openalex.org/W2154158661","https://openalex.org/W2159037096","https://openalex.org/W2194775991","https://openalex.org/W2751909359","https://openalex.org/W2756265196","https://openalex.org/W3101639073","https://openalex.org/W4205158112","https://openalex.org/W4245256608","https://openalex.org/W4320013936","https://openalex.org/W6743645564","https://openalex.org/W6785643234","https://openalex.org/W6805781942"],"related_works":["https://openalex.org/W3110074278","https://openalex.org/W2953246223","https://openalex.org/W4293320219","https://openalex.org/W4283584549","https://openalex.org/W2618858825","https://openalex.org/W2554314924","https://openalex.org/W2998859928","https://openalex.org/W3151498616","https://openalex.org/W4381885966","https://openalex.org/W4288256692"],"abstract_inverted_index":{"The":[0,113,166,234],"emerging":[1],"field":[2],"of":[3,20,37,46,50,66,76,88,127,160,193,212,247],"Computer":[4],"Vision":[5],"has":[6,69,116,169],"found":[7],"enormous":[8],"applications":[9],"in":[10,40,73,172,190],"our":[11],"day-to-day":[12],"lives":[13],"and":[14,33,43,131,182,250,257],"Medical":[15],"Image":[16],"Processing":[17],"is":[18,30],"one":[19],"the":[21,38,47,64,74,94,107,128,136,158,186,197,241],"most":[22],"prominent":[23],"fields":[24],"among":[25],"them.":[26],"Brain":[27,57],"Tumor":[28,58,180,256,258],"Segmentation":[29,59],"an":[31,85,140],"important":[32],"challenging":[34],"task":[35],"because":[36],"variety":[39],"shapes,":[41],"sizes":[42],"texture":[44],"content":[45],"various":[48,89],"types":[49],"brain":[51],"tumors.":[52],"Specifically,":[53],"MICCAI":[54],"BraTS":[55,82],"organizes":[56],"challenge":[60],"every":[61],"year.":[62],"Since":[63],"evolution":[65],"CNNs":[67],"it":[68],"obtained":[70],"state-of-the-art":[71,95,242],"results":[72],"majority":[75],"computer":[77],"vision":[78],"related":[79],"tasks.":[80],"On":[81],"Challenge":[83],"2017,":[84],"assemble":[86],"average":[87],"CNN":[90],"models":[91,118],"(EMMA)":[92],"holds":[93],"performance.":[96],"In":[97],"this":[98],"paper,":[99],"we":[100],"have":[101],"proposed":[102,114,167,235],"a":[103,132,161,173,204,219],"model":[104,138],"inspired":[105],"by":[106,143],"classic":[108],"Generative":[109],"Adversarial":[110],"Network":[111],"(GAN).":[112],"network":[115,188],"two":[117],"namely,":[119],"Generator":[120,137],"or":[121],"Segmentor":[122],"which":[123,134],"generates":[124],"label":[125],"map":[126],"input":[129],"image":[130],"Discriminator":[133,213],"helps":[135,189],"for":[139,177,196,252],"optimum":[141],"solution":[142],"taking":[144],"into":[145],"account":[146],"both":[147],"short":[148],"as":[149,151],"well":[150],"long-distance":[152],"spatial":[153],"correlations":[154],"between":[155],"pixels":[156],"with":[157],"help":[159],"novel":[162],"multi-scale":[163,205],"loss":[164,206,226],"function.":[165],"architecture":[168],"three":[170],"GANs":[171],"cascaded":[174],"fashion,":[175],"each":[176],"Whole":[178,255],"Tumor,":[179,184,254],"Core":[181,259],"Enhancing":[183,253],"where":[185],"former":[187],"effective":[191],"reduction":[192],"false":[194],"positives":[195],"later":[198],"networks.":[199],"Our":[200],"method":[201,236],"also":[202,228],"employs":[203],"function":[207,227],"derived":[208],"from":[209],"intermediate":[210],"layers":[211],"rather":[214],"than":[215,240],"depending":[216],"just":[217],"on":[218,232],"final":[220],"layer":[221],"cross-entropy":[222],"loss.":[223],"A":[224],"mutli-scale":[225],"reduces":[229],"unnecessary":[230],"smoothing":[231],"contours.":[233],"performed":[237],"comparatively":[238],"better":[239],"techniques,":[243],"having":[244],"Dice":[245],"scores":[246],"0.820,":[248],"0.874":[249],"0.783":[251],"respectively.":[260]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
