{"id":"https://openalex.org/W4391377903","doi":"https://doi.org/10.1109/medai59581.2023.00067","title":"A Novel Conditional Medical Image Generation GAN with Latent Space Optimal Transport","display_name":"A Novel Conditional Medical Image Generation GAN with Latent Space Optimal Transport","publication_year":2023,"publication_date":"2023-11-18","ids":{"openalex":"https://openalex.org/W4391377903","doi":"https://doi.org/10.1109/medai59581.2023.00067"},"language":"en","primary_location":{"id":"doi:10.1109/medai59581.2023.00067","doi":"https://doi.org/10.1109/medai59581.2023.00067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/medai59581.2023.00067","pdf_url":null,"source":{"id":"https://openalex.org/S7407088003","display_name":"IEEE International Conference on Medical Artificial Intelligence (MedAI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)","raw_type":"proceedings-article"},"volume":null,"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":null,"display_name":"Jun Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["School of Software Technology, Zhejiang University, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software Technology, Zhejiang University, Ningbo, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000102797","display_name":"Bohan Lei","orcid":"https://orcid.org/0009-0003-7309-1417","observed_orcids":["https://orcid.org/0009-0003-7309-1417"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bohan Lei","raw_affiliation_strings":["College of Computer Science &#x0026; Technology, Zhejiang University,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science &#x0026; Technology, Zhejiang University,Hangzhou,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073577367","display_name":"Xiaoyin Xu","orcid":"https://orcid.org/0000-0003-0813-7979","observed_orcids":["https://orcid.org/0000-0003-0813-7979"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyin Xu","raw_affiliation_strings":["College of Biomedical Engineering &#x0026; Instrument Science, Zhejiang University,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Biomedical Engineering &#x0026; Instrument Science, Zhejiang University,Hangzhou,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008666077","display_name":"Yueting Zhuang","orcid":"https://orcid.org/0000-0001-9017-2508","observed_orcids":["https://orcid.org/0000-0001-9017-2508"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueting Zhuang","raw_affiliation_strings":["College of Computer Science &#x0026; Technology, Zhejiang University,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science &#x0026; Technology, Zhejiang University,Hangzhou,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100744463","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0002-8681-5889","observed_orcids":["https://orcid.org/0000-0002-8681-5889"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["College of Computer Science &#x0026; Technology, Zhejiang University,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science &#x0026; Technology, Zhejiang University,Hangzhou,China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.4265,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72864682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"453","last_page":"458"},"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":0.9990000128746033,"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":0.9990000128746033,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T12357","display_name":"Digital Media Forensic Detection","score":9.999999747378752e-05,"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/conditional-image-generation","display_name":"conditional image generation","score":0.875},{"id":"https://openalex.org/keywords/medical-image-synthesis","display_name":"medical image synthesis","score":0.8199999928474426},{"id":"https://openalex.org/keywords/mode-collapse","display_name":"mode collapse","score":0.7540000081062317},{"id":"https://openalex.org/keywords/semi-discrete-optimal-transport","display_name":"semi-discrete optimal transport","score":0.8790000081062317},{"id":"https://openalex.org/keywords/latent-space-mapping","display_name":"latent space mapping","score":0.7160000205039978},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.871999979019165},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.7850000262260437},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.5899999737739563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6868186593055725},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6518751382827759},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5474792122840881},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5436086654663086},{"id":"https://openalex.org/C68028875","wikidata":"https://www.wikidata.org/wiki/Q7572595","display_name":"Space mapping","level":2,"score":0.5200855135917664},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.47683730721473694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47367626428604126},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45270228385925293},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40359488129615784},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3840680718421936},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37599241733551025},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08657416701316833},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/medai59581.2023.00067","doi":"https://doi.org/10.1109/medai59581.2023.00067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/medai59581.2023.00067","pdf_url":null,"source":{"id":"https://openalex.org/S7407088003","display_name":"IEEE International Conference on Medical Artificial Intelligence (MedAI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8048999905586243}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2132883347","https://openalex.org/W2194775991","https://openalex.org/W2605135824","https://openalex.org/W2785678896","https://openalex.org/W2893749619","https://openalex.org/W2896457183","https://openalex.org/W2962929605","https://openalex.org/W2963875852","https://openalex.org/W2963981733","https://openalex.org/W2994617167","https://openalex.org/W3096875747","https://openalex.org/W3106989878","https://openalex.org/W3207711866","https://openalex.org/W3216741342","https://openalex.org/W4200216121","https://openalex.org/W4205567685","https://openalex.org/W4317436377"],"related_works":["https://openalex.org/W4388764985","https://openalex.org/W4402915246","https://openalex.org/W4380301071","https://openalex.org/W4220950859","https://openalex.org/W3109809751","https://openalex.org/W4362551704","https://openalex.org/W2985563326","https://openalex.org/W4378363910","https://openalex.org/W4402782635","https://openalex.org/W4381747574"],"abstract_inverted_index":{"Generative":[0],"adversarial":[1],"network":[2],"(GAN)":[3],"is":[4],"widely":[5],"used":[6],"to":[7,24,48,75,80,95,130,147,162,194],"augment":[8],"training":[9,15],"set":[10],"in":[11,43,72,190],"medical":[12,85],"imaging.":[13],"However,":[14],"GANs":[16],"can":[17,37,67],"be":[18],"challenging,":[19],"as":[20],"they":[21],"are":[22],"susceptible":[23],"mode":[25,69],"collapse.":[26],"These":[27],"challenges":[28],"stem":[29],"from":[30,151],"the":[31,45,54,58,92,96,106,113,131,149,152,158,163,175,184],"fact":[32],"that":[33,66],"deep":[34],"neural":[35],"networks":[36],"only":[38],"represent":[39],"continuous":[40,153],"mappings,":[41],"while":[42],"GAN,":[44],"generator":[46],"aims":[47],"compute":[49],"discontinuous":[50],"transportation":[51],"maps":[52,126],"between":[53],"noise":[55],"distribution":[56,129,155],"and":[57,71,109,177],"data":[59,93,165],"distribution.":[60],"We":[61,170],"propose":[62],"a":[63,127],"novel":[64],"approach":[65],"overcome":[68],"collapse,":[70],"turn,":[73],"lead":[74],"improved":[76],"capability":[77],"by":[78,90,157],"GAN":[79,146],"generate":[81],"specific":[82],"classes":[83],"of":[84,187],"images.":[86,169],"Our":[87],"method":[88],"starts":[89],"mapping":[91,150,161],"manifold":[94],"latent":[97,114,133,138,154],"space":[98],"using":[99],"an":[100],"autoencoder":[101],"(AE).":[102],"Subsequently,":[103],"we":[104,117,144],"encode":[105],"sample":[107],"labels":[108],"integrate":[110],"them":[111],"with":[112,140],"representations.":[115],"Next,":[116],"employ":[118,145],"extended":[119,159],"semi-discrete":[120],"optimal":[121],"transport":[122],"(SDOT)":[123],"mapping,":[124],"which":[125],"Gaussian":[128],"empirical":[132],"distribution,":[134,166],"thereby":[135],"generating":[136,167,191],"new":[137],"representations":[139],"known":[141],"labels.":[142],"Finally,":[143],"establish":[148],"induced":[156],"SDOT":[160],"real":[164],"high-quality":[168],"conducted":[171],"extensive":[172],"experiments":[173],"on":[174],"DermaMNIST":[176],"BloodMNIST":[178],"datasets.":[179],"The":[180],"experimental":[181],"results":[182],"highlight":[183],"exceptional":[185],"performance":[186],"our":[188],"model":[189],"images":[192],"belonging":[193],"specified":[195],"classes.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2024-02-01T00:00:00"}
