{"id":"https://openalex.org/W2789728745","doi":"https://doi.org/10.1117/12.2293256","title":"Modelling the progression of Alzheimer's disease in MRI using generative adversarial networks","display_name":"Modelling the progression of Alzheimer's disease in MRI using generative adversarial networks","publication_year":2018,"publication_date":"2018-03-02","ids":{"openalex":"https://openalex.org/W2789728745","doi":"https://doi.org/10.1117/12.2293256","mag":"2789728745"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293256","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293256","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","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/A5026829432","display_name":"Christopher Bowles","orcid":"https://orcid.org/0000-0001-7374-288X"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Christopher Bowles","raw_affiliation_strings":["Imperial College London (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College London (United Kingdom)","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065874626","display_name":"Roger N. Gunn","orcid":"https://orcid.org/0000-0003-1181-5769"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Roger Gunn","raw_affiliation_strings":["Imanova Ltd. (United Kingdom)","Imperial College London (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imanova Ltd. (United Kingdom)","institution_ids":[]},{"raw_affiliation_string":"Imperial College London (United Kingdom)","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045287606","display_name":"Alexander Hammers","orcid":"https://orcid.org/0000-0001-9530-4848"},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alexander Hammers","raw_affiliation_strings":["King's College London (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King's College London (United Kingdom)","institution_ids":["https://openalex.org/I183935753"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006461848","display_name":"Daniel Rueckert","orcid":"https://orcid.org/0000-0002-5683-5889"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daniel Rueckert","raw_affiliation_strings":["Imperial College London (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College London (United Kingdom)","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.4265,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.96609466,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"55"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9955000281333923,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9854000210762024,"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/adversarial-system","display_name":"Adversarial system","score":0.710141658782959},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6976587772369385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6128876805305481},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5313447117805481},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.4740573763847351},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3392069935798645},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23078176379203796},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.11647659540176392}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.710141658782959},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6976587772369385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6128876805305481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5313447117805481},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.4740573763847351},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3392069935798645},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23078176379203796},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.11647659540176392}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293256","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293256","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W212537071","https://openalex.org/W1594041518","https://openalex.org/W1974874858","https://openalex.org/W2003919893","https://openalex.org/W2086978209","https://openalex.org/W2093290779","https://openalex.org/W2094843559","https://openalex.org/W2098176256","https://openalex.org/W2104048700","https://openalex.org/W2112076978","https://openalex.org/W2117340355","https://openalex.org/W2132458496","https://openalex.org/W2154758450","https://openalex.org/W2171051269","https://openalex.org/W2479644247","https://openalex.org/W2521028896","https://openalex.org/W2546066744","https://openalex.org/W2573380384","https://openalex.org/W2599354622","https://openalex.org/W2802245552","https://openalex.org/W2963684088","https://openalex.org/W2964024144","https://openalex.org/W4295274059","https://openalex.org/W4295521014","https://openalex.org/W4298289240","https://openalex.org/W6637568146","https://openalex.org/W6676769703","https://openalex.org/W6685352114","https://openalex.org/W6720963587","https://openalex.org/W6727501944","https://openalex.org/W6729001083","https://openalex.org/W6730746255","https://openalex.org/W6732211744","https://openalex.org/W6735913928","https://openalex.org/W6736155344","https://openalex.org/W6751374000","https://openalex.org/W6779669310"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Being":[0],"able":[1],"to":[2,24,91,116,136,157,170,175,179,207],"accurately":[3],"model":[4,92,132,178],"the":[5,14,19,26,39,51,54,57,79,99,123,131,147,158,171,177,182,190,194,201,204],"progression":[6,40,59],"of":[7,18,28,41,53,81,101,122,186,196,203],"Alzheimer\u2019s":[8],"disease":[9,29],"(AD)":[10],"is":[11],"important":[12],"for":[13,106],"diagnosis":[15],"and":[16,46,93,139,149,209,216],"prognosis":[17],"disease,":[20,55],"as":[21,23,60],"well":[22,156],"evaluate":[25],"effect":[27],"modifying":[30],"treatments.":[31],"Whilst":[32],"there":[33],"has":[34],"been":[35],"success":[36],"in":[37,146,189],"modeling":[38,56],"AD":[42,187],"related":[43],"clinical":[44],"biomarkers":[45],"image":[47,102,115],"derived":[48],"features":[49,124,142],"over":[50,161],"course":[52],"expected":[58],"observed":[61,159],"by":[62,199],"magnetic":[63],"resonance":[64],"(MR)":[65],"images":[66,96,108,212],"directly":[67,97],"remains":[68],"a":[69,88,162,168],"challenge.":[70],"Here,":[71],"we":[72],"apply":[73],"some":[74],"recently":[75],"developed":[76],"ideas":[77],"from":[78,143],"field":[80],"generative":[82],"adversarial":[83],"networks":[84],"(GANs)":[85],"which":[86],"provide":[87],"powerful":[89],"way":[90],"manipulate":[94],"MR":[95,114],"though":[98],"technique":[100],"arithmetic.":[103],"This":[104],"allows":[105],"synthetic":[107],"based":[109],"upon":[110],"an":[111],"individual":[112],"subject\u2019s":[113],"be":[117,134],"produced":[118],"expressing":[119],"different":[120],"levels":[121],"associated":[125],"with":[126,213],"AD.":[127],"We":[128,165,192],"demonstrate":[129],"how":[130],"can":[133],"used":[135],"both":[137],"introduce":[138],"remove":[140],"AD-like":[141],"two":[144],"regions":[145],"brain,":[148],"show":[150,193],"that":[151],"these":[152],"predicted":[153],"changes":[154,160],"correspond":[155],"longitudinal":[163],"examination.":[164],"also":[166],"propose":[167],"modification":[169,198],"GAN":[172],"training":[173],"procedure":[174],"encourage":[176],"better":[180],"represent":[181],"more":[183],"extreme":[184],"cases":[185],"present":[188],"dataset.":[191],"benefit":[195],"this":[197],"comparing":[200],"ability":[202],"resulting":[205],"models":[206],"encode":[208],"reconstruct":[210],"real":[211],"high":[214],"atrophy":[215],"other":[217],"unusual":[218],"features.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
