{"id":"https://openalex.org/W3001444784","doi":"https://doi.org/10.1109/cisp-bmei48845.2019.8965817","title":"Medical Image Augmentation Using Image Synthesis with Contextual Function","display_name":"Medical Image Augmentation Using Image Synthesis with Contextual Function","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3001444784","doi":"https://doi.org/10.1109/cisp-bmei48845.2019.8965817","mag":"3001444784"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei48845.2019.8965817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei48845.2019.8965817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5100688774","display_name":"Yin Xu","orcid":"https://orcid.org/0000-0002-7883-1002"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Xu Yin","raw_affiliation_strings":["Department of Computer Engineering, Inha University, Incheon, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Inha University, Incheon, South Korea","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100723042","display_name":"Yan Li","orcid":"https://orcid.org/0000-0003-1208-1226"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yan Li","raw_affiliation_strings":["Department of Computer Engineering, Inha University, Incheon, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Inha University, Incheon, South Korea","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100437188","display_name":"Xu Zhang","orcid":"https://orcid.org/0000-0001-9080-8027"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061420652","display_name":"Byeong\u2010Seok Shin","orcid":"https://orcid.org/0000-0001-7742-4846"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byeong-Seok Shin","raw_affiliation_strings":["Department of Computer Engineering, Inha University, Incheon, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Inha University, Incheon, South Korea","institution_ids":["https://openalex.org/I191879574"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100688774"],"corresponding_institution_ids":["https://openalex.org/I191879574"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47269387,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9998000264167786,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9998000264167786,"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":0.9954000115394592,"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"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/generality","display_name":"Generality","score":0.7858234643936157},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7609471082687378},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6842355728149414},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.654958963394165},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6409528255462646},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6093984246253967},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5618241429328918},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5575185418128967},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5370250344276428},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5366328358650208},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.536057710647583},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4703878164291382},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4175233244895935},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41538140177726746},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41332706809043884}],"concepts":[{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.7858234643936157},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609471082687378},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6842355728149414},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.654958963394165},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6409528255462646},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6093984246253967},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5618241429328918},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5575185418128967},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5370250344276428},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5366328358650208},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.536057710647583},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4703878164291382},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4175233244895935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41538140177726746},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41332706809043884},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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:10.1109/cisp-bmei48845.2019.8965817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei48845.2019.8965817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W2183341477","https://openalex.org/W2475287302","https://openalex.org/W2567079332","https://openalex.org/W2583638424","https://openalex.org/W2603777577","https://openalex.org/W2743780012","https://openalex.org/W2745006834","https://openalex.org/W2768959015","https://openalex.org/W2949117887","https://openalex.org/W2949799260","https://openalex.org/W2962793481","https://openalex.org/W2962947361","https://openalex.org/W2963330667","https://openalex.org/W2963890275","https://openalex.org/W2963942586","https://openalex.org/W2964309429","https://openalex.org/W6639824700","https://openalex.org/W6734074887","https://openalex.org/W6742537510","https://openalex.org/W6745992979","https://openalex.org/W6750298367"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W2280377497","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W3174044702","https://openalex.org/W2967848559","https://openalex.org/W4283803360"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"technology":[2],"has":[3,46],"been":[4],"widely":[5],"used":[6],"in":[7,64],"medical":[8,11,65,100],"research.":[9],"For":[10],"images":[12,33,106],"that":[13,77,110],"normally":[14],"contain":[15],"more":[16,50,52],"complicated":[17],"distributions":[18],"than":[19,82],"ordinary":[20],"images,":[21],"existing":[22,122],"methods":[23],"have":[24],"tended":[25],"to":[26,48,99],"show":[27,109],"poor":[28],"generality":[29],"when":[30],"dealing":[31],"with":[32,71,104],"of":[34,43,61],"diverse":[35],"distributions.":[36],"In":[37,54],"recent":[38],"years,":[39],"the":[40],"new":[41,73],"method":[42,112],"generative":[44,62],"model":[45],"begun":[47],"receive":[49],"and":[51,94,107,115],"attention.":[53],"this":[55,97],"paper,":[56],"we":[57,86],"focus":[58],"on":[59],"applications":[60],"models":[63,123],"imaging.":[66],"We":[67],"propose":[68],"a":[69,72,90],"framework":[70,98],"contextual":[74],"loss":[75],"function":[76],"can":[78],"preserve":[79],"contexts":[80],"better":[81],"traditional":[83],"methods.":[84],"Then":[85],"treat":[87],"it":[88],"as":[89],"data":[91],"augmentation":[92],"operation":[93],"successfully":[95],"apply":[96],"image":[101],"segmentation.":[102],"Experiments":[103],"generated":[105],"segmentation":[108],"our":[111],"is":[113],"accurate":[114],"robust":[116],"for":[117],"maintaining":[118],"semantics,":[119],"outperforming":[120],"two":[121],"under":[124],"comparison.":[125]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
