{"id":"https://openalex.org/W6945853913","doi":"https://doi.org/10.26092/elib/1941","title":"Hybrid Deep Learning: how combining data-driven and model-based approaches solves inverse problems in computed tomography and beyond","display_name":"Hybrid Deep Learning: how combining data-driven and model-based approaches solves inverse problems in computed tomography and beyond","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W6945853913","doi":"https://doi.org/10.26092/elib/1941"},"language":"en","primary_location":{"id":"pmh:oai:media.suub.uni-bremen.de:Publications/elib/6379","is_oa":true,"landing_page_url":"https://media.suub.uni-bremen.de/handle/elib/6379","pdf_url":null,"source":{"id":"https://openalex.org/S4306402486","display_name":"Media (https://www.suub.uni-bremen.de/)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180437899","host_organization_name":"University of Bremen","host_organization_lineage":["https://openalex.org/I180437899"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://media.suub.uni-bremen.de/handle/elib/6379","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Schmidt, Maximilian","orcid":"https://orcid.org/0000-0001-8710-1389"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Schmidt, Maximilian","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8710-1389","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.09719999879598618,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.09719999879598618,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.05139999836683273,"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/T13398","display_name":"Data Analysis with R","score":0.04879999905824661,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4713999927043915},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3377000093460083},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.31610000133514404},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3052000105381012},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.29120001196861267},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.28949999809265137}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47699999809265137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4713999927043915},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.24619999527931213}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:media.suub.uni-bremen.de:Publications/elib/6379","is_oa":true,"landing_page_url":"https://media.suub.uni-bremen.de/handle/elib/6379","pdf_url":null,"source":{"id":"https://openalex.org/S4306402486","display_name":"Media (https://www.suub.uni-bremen.de/)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180437899","host_organization_name":"University of Bremen","host_organization_lineage":["https://openalex.org/I180437899"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},{"id":"doi:10.26092/elib/1941","is_oa":true,"landing_page_url":"https://doi.org/10.26092/elib/1941","pdf_url":null,"source":{"id":"https://openalex.org/S7407052977","display_name":"Staats- und Universit\u00e4tsbibliothek Bremen","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:media.suub.uni-bremen.de:Publications/elib/6379","is_oa":true,"landing_page_url":"https://media.suub.uni-bremen.de/handle/elib/6379","pdf_url":null,"source":{"id":"https://openalex.org/S4306402486","display_name":"Media (https://www.suub.uni-bremen.de/)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180437899","host_organization_name":"University of Bremen","host_organization_lineage":["https://openalex.org/I180437899"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Artificial":[0],"neural":[1,125,177],"networks":[2,40,178],"from":[3,34,265,272],"the":[4,12,15,38,61,78,90,95,115,169,194,214,250,256],"field":[5],"of":[6,14,44,46,71,80,98,223],"deep":[7,104,128,155,170,274],"learning":[8,27,105,129,156,275],"are":[9,50,112,162,253],"increasingly":[10],"becoming":[11],"state":[13],"art":[16],"in":[17,30,63,183,211,217,260],"more":[18,20],"and":[19,134,164,174,206,225,237,244],"applications.":[21],"Their":[22],"success":[23],"is":[24,106,209,235],"based":[25],"on":[26,114],"complex":[28],"relationships":[29],"a":[31,64,189,230],"system":[32],"purely":[33,102],"data.":[35],"For":[36,227],"this,":[37],"data-driven":[39,103,133],"often":[41,74],"require":[42],"hundreds":[43],"thousands":[45],"reference":[47],"examples.":[48],"They":[49,66],"contrasted":[51],"by":[52],"model-based":[53,86,135,258],"approaches":[54,87,136,157,252],"that":[55,118,131,249],"use":[56,101],"mathematical":[57],"methods":[58,259],"to":[59,100,192,202,255],"describe":[60],"processes":[62],"system.":[65],"work":[67],"without":[68],"large":[69],"amounts":[70],"data":[72,99,224,246],"but":[73],"cannot":[75,119],"cover":[76],"all":[77],"nuances":[79],"an":[81],"application.":[82],"In":[83,109,141,150],"inverse":[84,160,218,266],"problems,":[85,219],"have":[88],"been":[89],"standard":[91],"so":[92],"far.":[93],"Here,":[94],"necessary":[96],"amount":[97],"usually":[107],"unavailable.":[108],"addition,":[110],"requirements":[111],"placed":[113],"model":[116],"properties":[117],"always":[120],"be":[121],"proven":[122],"for":[123,158,242],"classical":[124],"networks.":[126],"Hybrid":[127],"models":[130,195],"combine":[132],"can":[137,268],"solve":[138],"these":[139],"challenges.":[140],"recent":[142],"years,":[143],"their":[144,204],"research":[145],"has":[146],"steadily":[147],"gained":[148],"importance.":[149],"this":[151,228],"thesis,":[152],"several":[153],"hybrid":[154,251,273],"solving":[159],"problems":[161,267],"presented":[163,236],"further":[165],"developed.":[166],"These":[167],"include":[168],"image":[171],"prior":[172],"(DIP)":[173],"conditional":[175],"invertible":[176],"(CINN).":[179],"The":[180,239],"reconstruction":[181],"problem":[182],"computed":[184],"tomography":[185],"(CT)":[186],"serves":[187],"as":[188,199,201,221],"central":[190],"example":[191],"compare":[193],"with":[196],"each":[197],"other,":[198],"well":[200],"reveal":[203],"strengths":[205],"weaknesses.":[207],"This":[208],"done":[210],"particular":[212],"concerning":[213],"unique":[215],"challenges":[216],"such":[220],"lack":[222],"ill-posedness.":[226],"purpose,":[229],"realistic":[231],"medical":[232,243],"CT":[233],"dataset":[234],"used.":[238],"performed":[240],"comparison":[241],"industrial":[245],"clearly":[247],"shows":[248],"superior":[254],"classical,":[257],"many":[261],"areas.":[262],"Countless":[263],"applications":[264],"thus":[269],"already":[270],"benefit":[271],"approaches.":[276]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}