{"id":"https://openalex.org/W7135030554","doi":"https://doi.org/10.48550/arxiv.2603.10519","title":"Visually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement","display_name":"Visually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135030554","doi":"https://doi.org/10.48550/arxiv.2603.10519"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.10519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10519","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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.2603.10519","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128903194","display_name":"Xin Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123855358","display_name":"Junjie Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Junjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006758777","display_name":"Qingshan Hou","orcid":"https://orcid.org/0000-0002-3839-1399"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Qingshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128847888","display_name":"Peng Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128853704","display_name":"Jinzhu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jinzhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128800625","display_name":"Xiaoli Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123924308","display_name":"Osmar R Za\u00efane","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaiane, Osmar R.","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":0.9427000284194946,"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":0.9427000284194946,"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/T11448","display_name":"Face recognition and analysis","score":0.012299999594688416,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.007400000002235174,"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/feature","display_name":"Feature (linguistics)","score":0.4749000072479248},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4684000015258789},{"id":"https://openalex.org/keywords/controllability","display_name":"Controllability","score":0.4122999906539917},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.40790000557899475},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.39590001106262207},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3662000000476837},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3580999970436096},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.3531999886035919},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.3490999937057495}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7979999780654907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5516999959945679},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C48209547","wikidata":"https://www.wikidata.org/wiki/Q1331104","display_name":"Controllability","level":2,"score":0.4122999906539917},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3531999886035919},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3490999937057495},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.34389999508857727},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3244999945163727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31769999861717224},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.3163999915122986},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C177650935","wikidata":"https://www.wikidata.org/wiki/Q1760303","display_name":"Netlist","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25429999828338623},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.10519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10519","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.10519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10519","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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":{"Medical":[0],"image":[1],"synthesis":[2],"is":[3,138],"crucial":[4],"for":[5],"alleviating":[6],"data":[7],"scarcity":[8],"and":[9,31,50,127],"privacy":[10],"constraints.":[11],"However,":[12],"fine-tuning":[13],"general":[14],"text-to-image":[15],"(T2I)":[16],"models":[17],"remains":[18],"challenging,":[19],"mainly":[20],"due":[21],"to":[22,79],"the":[23,45],"significant":[24],"modality":[25],"gap":[26],"between":[27,47],"complex":[28],"visual":[29,77],"details":[30],"abstract":[32],"clinical":[33],"text.":[34],"In":[35],"addition,":[36],"semantic":[37,86],"entanglement":[38],"persists,":[39],"where":[40],"coarse-grained":[41],"text":[42,83],"embeddings":[43],"blur":[44],"boundary":[46],"anatomical":[48],"structures":[49],"imaging":[51],"styles,":[52],"thus":[53],"weakening":[54],"controllability":[55],"during":[56],"generation.":[57],"To":[58],"address":[59],"this,":[60],"we":[61],"propose":[62],"a":[63,70,89,99],"Visually-Guided":[64],"Text":[65],"Disentanglement":[66],"framework.":[67],"We":[68],"introduce":[69],"cross-modal":[71],"latent":[72],"alignment":[73],"mechanism":[74],"that":[75,116],"leverages":[76],"priors":[78],"explicitly":[80],"disentangle":[81],"unstructured":[82],"into":[84,98],"independent":[85],"representations.":[87],"Subsequently,":[88],"Hybrid":[90],"Feature":[91],"Fusion":[92],"Module":[93],"(HFFM)":[94],"injects":[95],"these":[96],"features":[97],"Diffusion":[100],"Transformer":[101],"(DiT)":[102],"through":[103],"separated":[104],"channels,":[105],"enabling":[106],"fine-grained":[107],"structural":[108],"control.":[109],"Experimental":[110],"results":[111],"in":[112,122],"three":[113],"datasets":[114],"demonstrate":[115],"our":[117],"method":[118],"outperforms":[119],"existing":[120],"approaches":[121],"terms":[123],"of":[124],"generation":[125],"quality":[126],"significantly":[128],"improves":[129],"performance":[130],"on":[131],"downstream":[132],"classification":[133],"tasks.":[134],"The":[135],"source":[136],"code":[137],"available":[139],"at":[140],"https://github.com/hx111/VG-MedGen.":[141]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-13T00:00:00"}