{"id":"https://openalex.org/W2997763025","doi":"https://doi.org/10.1145/3364836.3364865","title":"ScaDO Net","display_name":"ScaDO Net","publication_year":2019,"publication_date":"2019-08-24","ids":{"openalex":"https://openalex.org/W2997763025","doi":"https://doi.org/10.1145/3364836.3364865","mag":"2997763025"},"language":"en","primary_location":{"id":"doi:10.1145/3364836.3364865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","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/A5100351699","display_name":"He Wang","orcid":"https://orcid.org/0000-0003-1679-0337"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"He Wang","raw_affiliation_strings":["Institute of Basic Medical Sciences, China Academy of Medical Sciences, Peking Union Medical College, China"],"affiliations":[{"raw_affiliation_string":"Institute of Basic Medical Sciences, China Academy of Medical Sciences, Peking Union Medical College, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003289067","display_name":"Weiwei Zhang","orcid":"https://orcid.org/0000-0001-7799-833X"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Zhang","raw_affiliation_strings":["Institute of Basic Medical Sciences, China Academy of Medical Sciences, Peking Union Medical College, China"],"affiliations":[{"raw_affiliation_string":"Institute of Basic Medical Sciences, China Academy of Medical Sciences, Peking Union Medical College, China","institution_ids":["https://openalex.org/I200296433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100351699"],"corresponding_institution_ids":["https://openalex.org/I200296433"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15746346,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"39","issue":null,"first_page":"143","last_page":"151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":0.9995999932289124,"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":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.7782996892929077},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.7545149326324463},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6245028972625732},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6044641137123108},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.5963757634162903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5408438444137573},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5398385524749756},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4258199632167816},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.41074588894844055},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3548571765422821},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35018327832221985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3234080672264099},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13080650568008423},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08678564429283142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7782996892929077},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.7545149326324463},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6245028972625732},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6044641137123108},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5963757634162903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5408438444137573},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5398385524749756},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4258199632167816},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.41074588894844055},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3548571765422821},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35018327832221985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3234080672264099},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13080650568008423},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08678564429283142},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3364836.3364865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W125636640","https://openalex.org/W1903029394","https://openalex.org/W2018662705","https://openalex.org/W2069331971","https://openalex.org/W2083099567","https://openalex.org/W2114740909","https://openalex.org/W2301358467","https://openalex.org/W2343172899","https://openalex.org/W2460515352","https://openalex.org/W2567599812","https://openalex.org/W2589647984","https://openalex.org/W2607256519","https://openalex.org/W2621028221","https://openalex.org/W2889890911","https://openalex.org/W2891155035","https://openalex.org/W2938458886","https://openalex.org/W2952632681","https://openalex.org/W2963446712","https://openalex.org/W2988396473","https://openalex.org/W3038071008","https://openalex.org/W4231091991","https://openalex.org/W4289543463","https://openalex.org/W4308909683","https://openalex.org/W6725739302"],"related_works":["https://openalex.org/W3104750253","https://openalex.org/W3021239166","https://openalex.org/W2586273397","https://openalex.org/W2390936256","https://openalex.org/W2483429559","https://openalex.org/W2016385589","https://openalex.org/W2009559548","https://openalex.org/W2995343971","https://openalex.org/W2982536526","https://openalex.org/W2992221004"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,25,92,105,126],"neuroanatomy":[2],"based":[3,54],"on":[4,167],"fully":[5],"convolutional":[6],"networks":[7],"(FCNs)":[8],"has":[9,201],"been":[10],"proven":[11],"to":[12,56,74,118],"be":[13],"powerful":[14],"in":[15,37,132],"numerous":[16],"medical":[17],"applications":[18],"with":[19,83,155,160],"excellent":[20],"performance.":[21],"However,":[22],"the":[23,29,58,76,90,109,115,120,124,161,164,175,198],"lack":[24],"annotated":[26],"data":[27],"and":[28,94,122,188],"inconsistency":[30],"between":[31],"different":[32,168],"datasets":[33,171],"make":[34],"it":[35],"difficult":[36],"training":[38],"a":[39,51,103,151],"deep":[40],"network":[41],"model":[42,55],"for":[43,186,206],"brain":[44,59,79,170,183],"segmentation.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49,135],"propose":[50],"3D":[52,77],"patch-wise":[53,78],"learn":[57],"segmentation,":[60,184],"namely":[61],"ScaDO":[62,116,177],"net":[63,68,117,178],"(Scaffold-Dense-Octave":[64],"net).":[65],"This":[66],"scaffold-like":[67],"is":[69,112],"designed":[70],"by":[71,102],"Scaffold":[72],"blocks":[73],"perform":[75],"segmentation":[80],"task.":[81],"Even":[82],"more":[84],"than":[85,204],"one":[86,194],"hundred":[87,195],"convolution":[88,111,196],"layers,":[89,197],"number":[91,125],"parameters":[93],"FLOPs":[95,121],"(Float":[96],"Point":[97],"Operations)":[98],"are":[99],"strictly":[100],"controlled":[101],"set":[104],"well-defined":[106],"hyperparameters.":[107],"Furthermore,":[108],"octave":[110],"incorporated":[113],"into":[114],"reduce":[119],"keep":[123],"parameters.":[127],"To":[128],"give":[129],"reasonable":[130],"suggestions":[131],"practical":[133],"applications,":[134],"have":[136,172],"conducted":[137],"ablation":[138],"studies":[139],"about":[140],"FLOPs-DICE":[141],"trade-off,":[142],"which":[143],"showed":[144],"that":[145,174],"setting":[146],"appropriate":[147],"hyperparameters":[148],"would":[149],"achieve":[150,180],"high":[152],"DICE":[153],"value":[154],"relatively":[156],"small":[157,187],"FLOPs.":[158],"Compared":[159],"state-of-the-art":[162],"methods,":[163],"experimental":[165],"results":[166],"heterogeneous":[169],"demonstrated":[173],"proposed":[176,199],"can":[179],"an":[181],"accurate":[182],"even":[185],"delicate":[189],"subcortical":[190],"structures.":[191],"With":[192],"over":[193],"method":[200],"as":[202],"shorter":[203],"40s":[205],"per":[207],"MRI":[208],"volume.":[209]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-01-10T00:00:00"}