{"id":"https://openalex.org/W3106860801","doi":"https://doi.org/10.1145/3400286.3418244","title":"Using Synthesized Data to Train Deep Neural Net with Few Data","display_name":"Using Synthesized Data to Train Deep Neural Net with Few Data","publication_year":2020,"publication_date":"2020-10-13","ids":{"openalex":"https://openalex.org/W3106860801","doi":"https://doi.org/10.1145/3400286.3418244","mag":"3106860801"},"language":"en","primary_location":{"id":"doi:10.1145/3400286.3418244","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3400286.3418244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","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/A5065127601","display_name":"Cheng-Shao Chiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng-Shao Chiang","raw_affiliation_strings":["LiLee Systems Inc, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LiLee Systems Inc, Taipei, Taiwan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103938490","display_name":"Chi-Sheng Daniel Shih","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chi-Sheng Daniel Shih","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9493,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81423948,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10916","display_name":"Surgical Simulation and Training","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9851999878883362,"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/computer-science","display_name":"Computer science","score":0.8325983881950378},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7548452615737915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7089313864707947},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.6305951476097107},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5831692814826965},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5718494653701782},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5257502198219299},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5028674006462097},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.47286200523376465},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46468615531921387},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.45750191807746887},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4278843402862549},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36408817768096924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8325983881950378},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7548452615737915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7089313864707947},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.6305951476097107},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5831692814826965},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5718494653701782},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5257502198219299},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5028674006462097},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.47286200523376465},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46468615531921387},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.45750191807746887},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4278843402862549},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36408817768096924},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3400286.3418244","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3400286.3418244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2604262106","https://openalex.org/W2687693326","https://openalex.org/W2789702442","https://openalex.org/W2963271314","https://openalex.org/W2990138404"],"related_works":["https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W83146503","https://openalex.org/W2163707935","https://openalex.org/W202723009","https://openalex.org/W2145955964","https://openalex.org/W2188612292","https://openalex.org/W4206462905","https://openalex.org/W2165396616"],"abstract_inverted_index":{"As":[0],"Computer-Assisted":[1],"Surgery":[2],"(CAS)":[3],"getting":[4],"popular,":[5],"more":[6,8,187],"and":[7,109,127,163,189],"research":[9],"has":[10],"been":[11],"conducted":[12],"to":[13,38,81,105,116,134],"help":[14],"surgeons":[15],"operate.":[16],"We":[17,97,138],"aim":[18],"at":[19],"the":[20,24,32,44,62,69,83,103,107,111,143,184],"semantic":[21,29,88],"segmentation":[22,30,89],"in":[23,43,94,123,159,166],"endoscopy":[25,63],"surgery":[26,64],"scenario":[27],"because":[28],"is":[31,66,74],"first":[33],"step":[34],"for":[35],"a":[36,87,99,118,124,129,136],"computer":[37],"grasp":[39],"what":[40],"shows":[41],"up":[42],"vision":[45],"of":[46,56,61,71,85,120,132,197],"an":[47],"endoscope.":[48],"However,":[49],"modern":[50],"Deep":[51],"Learning":[52],"algorithms":[53,73],"need":[54],"myriads":[55],"training":[57,86,121],"data.":[58,198],"Since":[59],"data":[60,93,122,133],"scene":[65],"relatively":[67],"scarce,":[68],"performance":[70],"existing":[72],"thus":[75],"rather":[76],"limited.":[77],"Therefore,":[78],"we":[79],"tried":[80],"solve":[82],"problem":[84],"network":[90],"with":[91],"few":[92],"this":[95],"work.":[96],"propose":[98],"proof-of-concept":[100],"system":[101,114],"offering":[102],"ability":[104],"enlarge":[106],"dataset":[108,144],"improve":[110],"performance.":[112],"The":[113],"aims":[115],"synthesize":[117],"pair":[119],"single":[125],"pass":[126],"provides":[128],"sufficient":[130],"amount":[131,196],"train":[135],"network.":[137],"evaluated":[139],"our":[140,181],"method":[141,154,182],"using":[142],"provided":[145],"by":[146],"MICCAI":[147],"2018":[148],"Robotic":[149],"Scene":[150],"Segmentation":[151],"Sub-Challenge.":[152],"Our":[153],"yielded":[155],"11.79%":[156],"mIoU":[157,165],"improvement":[158],"recognizing":[160,167],"anatomical":[161,171],"objects":[162,172],"2.2%":[164],"surgical":[168],"instruments.":[169],"Recognizing":[170],"accurately":[173],"would":[174],"definitely":[175],"benefit":[176],"CAS.":[177],"Preliminary":[178],"results":[179],"suggest":[180],"helps":[183],"classifier":[185],"become":[186],"robust":[188],"accurate":[190],"even":[191],"if":[192],"not":[193],"having":[194],"large":[195]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
