{"id":"https://openalex.org/W7170033800","doi":"https://doi.org/10.48550/arxiv.2607.18882","title":"Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches","display_name":"Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches","publication_year":2026,"publication_date":"2026-07-21","ids":{"openalex":"https://openalex.org/W7170033800","doi":"https://doi.org/10.48550/arxiv.2607.18882"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.18882","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18882","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.18882","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064229790","display_name":"Rick Wilming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wilming, Rick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143449135","display_name":"Irem Ozseker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ozseker, Irem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098767807","display_name":"Luca Matteo Cornils","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cornils, Luca Matteo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058501833","display_name":"Ahc\u00e8ne Boubekki","orcid":"https://orcid.org/0000-0003-1606-1513"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boubekki, Ahc\u00e8ne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112943674","display_name":"Benedict Clark","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Clark, Benedict","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065845667","display_name":"Danny Panknin","orcid":"https://orcid.org/0000-0002-1594-5045"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Panknin, Danny","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143417716","display_name":"Stefan Haufe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haufe, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"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":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.41920000314712524,"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":0.41920000314712524,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.08820000290870667,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.08079999685287476,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6134999990463257},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5152000188827515},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4909999966621399},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.44110000133514404},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4165000021457672},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.37860000133514404},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.3472999930381775},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.3450999855995178}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7799999713897705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6841999888420105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6134999990463257},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4268999993801117},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.30660000443458557},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.2700999975204468},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.18882","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18882","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.18882","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18882","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Validating":[0],"Explainable":[1],"Artificial":[2],"Intelligence":[3],"(XAI)":[4],"methods":[5,183],"in":[6,70,157,184],"medical":[7,185],"imaging":[8,122],"requires":[9],"ground-truth":[10],"data":[11,123,179],"with":[12,66,85,138],"known":[13],"locations":[14],"of":[15,167],"informative":[16],"features.":[17],"However,":[18],"current":[19],"approaches":[20,115],"rely":[21],"on":[22,32,109,118],"expert":[23],"annotations,":[24],"which":[25,46,73],"are":[26,116,146],"prone":[27],"to":[28,40,140,148],"labeling":[29],"errors,":[30],"or":[31,43],"hand-crafted":[33],"artificial":[34],"perturbations":[35],"superimposed":[36],"onto":[37],"healthy":[38,153],"images":[39,65,156],"mimic":[41],"lesions":[42,68],"malignant":[44],"features,":[45],"lack":[47],"clinical":[48],"realism.":[49],"We":[50,82],"present":[51],"Local":[52],"Label-Informed":[53],"Feature":[54],"Transfer":[55],"(LLIFT),":[56],"a":[57,90,104],"framework":[58],"for":[59,180],"generating":[60],"semi-synthetic":[61],"brain":[62,119],"magnetic":[63,120],"resonance":[64,121],"realistic":[67],"placed":[69],"user-controlled":[71],"regions,":[72],"does":[74],"not":[75],"require":[76],"pixel-level":[77],"lesion":[78,168],"annotations":[79],"during":[80],"training.":[81],"implement":[83],"LLIFT":[84],"two":[86],"generative":[87],"paradigms:":[88],"LLIFT-GAN,":[89],"custom":[91],"GAN":[92],"that":[93,145],"learns":[94],"pathological":[95,143,155],"features":[96],"from":[97,125],"binary":[98],"class":[99],"labels":[100],"alone,":[101],"and":[102,154],"LLIFT-DM,":[103],"diffusion-based":[105],"inpainting":[106],"pipeline":[107],"conditioned":[108],"bounding-box":[110],"masks":[111],"via":[112],"ControlNet.":[113],"Both":[114],"evaluated":[117],"derived":[124],"the":[126,141,149,158,165],"Human":[127],"Connectome":[128],"Project.":[129],"In":[130],"evaluations,":[131],"both":[132],"achieve":[133],"Fr\u00e9chet":[134],"Inception":[135],"Distance":[136],"scores,":[137],"respect":[139],"real":[142],"distribution,":[144],"comparable":[147],"inter-class":[150],"reference":[151],"between":[152],"given":[159],"dataset.":[160],"Furthermore,":[161],"qualitative":[162],"inspection":[163],"confirms":[164],"realism":[166],"structures.":[169],"The":[170],"resulting":[171],"benchmark":[172],"datasets":[173],"provide":[174],"spatially":[175],"controlled":[176],"ground":[177],"truth":[178],"evaluating":[181],"XAI":[182],"imaging.":[186]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-23T00:00:00"}
