{"id":"https://openalex.org/W4416078452","doi":"https://doi.org/10.1109/ipta66025.2025.11222025","title":"Pathological Region Inpainting in MRI Data Using Generative AI","display_name":"Pathological Region Inpainting in MRI Data Using Generative AI","publication_year":2025,"publication_date":"2025-10-13","ids":{"openalex":"https://openalex.org/W4416078452","doi":"https://doi.org/10.1109/ipta66025.2025.11222025"},"language":null,"primary_location":{"id":"doi:10.1109/ipta66025.2025.11222025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta66025.2025.11222025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Fourteenth International Conference on Image Processing, Theory, Tools & Applications (IPTA)","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":null,"display_name":"Maxim Sharaev","orcid":"https://orcid.org/0000-0002-5670-2891"},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Maxim Sharaev","raw_affiliation_strings":["Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120328401","display_name":"Nadezhda Alsakhanova","orcid":null},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Nadezhda Alsakhanova","raw_affiliation_strings":["Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Svetlana Illarionova","orcid":null},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Svetlana Illarionova","raw_affiliation_strings":["Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia","institution_ids":["https://openalex.org/I125989756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120048281","display_name":"Pavel Bartenev","orcid":null},"institutions":[{"id":"https://openalex.org/I125989756","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143","country_code":"RU","type":"education","lineage":["https://openalex.org/I125989756"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Pavel Bartenev","raw_affiliation_strings":["Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia"],"affiliations":[{"raw_affiliation_string":"Skolkovo Institute of Science and Technology,BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory,Moscow,Russia","institution_ids":["https://openalex.org/I125989756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125989756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34660021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"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":0.7921000123023987,"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":0.7921000123023987,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.03400000184774399,"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":0.022199999541044235,"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/inpainting","display_name":"Inpainting","score":0.7361000180244446},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5812000036239624},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5550000071525574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5073999762535095},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4921000003814697},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48750001192092896},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.47350001335144043},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.45159998536109924}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7893000245094299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7368999719619751},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.7361000180244446},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5550000071525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4921000003814697},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48750001192092896},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.47350001335144043},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4546999931335449},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.398499995470047},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.35989999771118164},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.26980000734329224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta66025.2025.11222025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta66025.2025.11222025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Fourteenth International Conference on Image Processing, Theory, Tools & Applications (IPTA)","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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0],"imaging":[1],"datasets":[2],"are":[3,17],"often":[4,69],"constrained":[5],"by":[6,51],"acquisition":[7],"costs,":[8],"scarce":[9],"patient":[10],"cohorts,":[11],"and":[12,60,103,138,163,180],"privacy":[13],"restrictions.":[14],"These":[15],"limitations":[16],"particularly":[18],"acute":[19],"for":[20,41,85],"subtle":[21],"neuropathologies":[22],"such":[23],"as":[24,64],"focal":[25],"cortical":[26],"dysplasia":[27],"(FCD),":[28],"where":[29],"conventional":[30],"MRI":[31,111],"struggles":[32],"to":[33,106,128,168],"detect":[34],"epileptogenic":[35],"regions\u2014":[36],"posing":[37],"a":[38,48,80,96,124],"critical":[39],"bottleneck":[40],"computer-aided":[42],"diagnosis.":[43],"Deep":[44],"generative":[45,82,97],"models":[46],"offer":[47],"promising":[49],"solution":[50],"synthesizing":[52,86],"realistic":[53,91,135],"data.":[54],"Yet,":[55],"in":[56,157,175],"scenarios":[57],"with":[58,90],"limited":[59,100,177],"complex":[61],"training":[62,149],"data\u2014such":[63],"3D":[65],"brain":[66],"MRI-generated":[67],"images":[68,89],"suffer":[70],"from":[71],"inadequate":[72],"diversity.":[73,120],"To":[74],"overcome":[75],"these":[76],"limitations,":[77],"we":[78],"introduce":[79,123],"novel":[81],"inpainting":[83],"approach":[84,94],"highquality":[87],"medical":[88],"pathologies.":[92],"Our":[93],"trains":[95],"model":[98,127],"on":[99],"pathological":[101,178],"data":[102,147,179],"leverages":[104],"it":[105],"inpaint":[107],"lesions":[108],"into":[109,148],"healthy":[110],"scans,":[112],"preserving":[113],"anatomical":[114],"context":[115],"while":[116],"enhancing":[117,181],"synthetic":[118,146],"image":[119],"We":[121],"also":[122],"dedicated":[125],"mask-generation":[126],"synthesize":[129],"biologically":[130],"plausible":[131],"pathology":[132],"masks,":[133],"ensuring":[134],"lesion":[136,158],"morphology":[137],"distribution":[139],"across":[140],"the":[141,172],"brain.":[142],"The":[143],"addition":[144],"of":[145],"dataset":[150],"(neither":[151],"validation":[152],"or":[153],"test)":[154],"improves":[155],"performance":[156],"segmentation":[159],"task.":[160],"Both":[161],"precision":[162],"Dice":[164],"score":[165],"increase":[166],"up":[167],"20%,":[169],"which":[170],"demonstrate":[171],"method's":[173],"effectiveness":[174],"augmenting":[176],"FCD":[182],"detection.":[183]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-11-10T00:00:00"}