{"id":"https://openalex.org/W7166587727","doi":"https://doi.org/10.48550/arxiv.2606.27794","title":"Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation","display_name":"Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166587727","doi":"https://doi.org/10.48550/arxiv.2606.27794"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27794","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27794","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.2606.27794","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139586947","display_name":"Jian Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139598251","display_name":"Cheng Zhen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhen, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056795737","display_name":"P Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Pingping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139604266","display_name":"Rui Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109481613","display_name":"Yanan Lv","orcid":"https://orcid.org/0000-0003-0653-3636"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Yanan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139546654","display_name":"Yili Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Yili","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136956426","display_name":"Huan Bi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bi, Huan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139608586","display_name":"Haojie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haojie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029653356","display_name":"H\u00fcseyin Ozan \u04aairkino\u011flu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Huchuan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8610000014305115,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8610000014305115,"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":0.055399999022483826,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.018400000408291817,"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/segmentation","display_name":"Segmentation","score":0.7049999833106995},{"id":"https://openalex.org/keywords/color-constancy","display_name":"Color constancy","score":0.623199999332428},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5878000259399414},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5428000092506409},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5314000248908997},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.46959999203681946},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.435699999332428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4235999882221222}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7932000160217285},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7049999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6901999711990356},{"id":"https://openalex.org/C187888035","wikidata":"https://www.wikidata.org/wiki/Q2563885","display_name":"Color constancy","level":3,"score":0.623199999332428},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5878000259399414},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5428000092506409},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5044999718666077},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.46959999203681946},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.40230000019073486},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.39320001006126404},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26759999990463257},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27794","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27794","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.2606.27794","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27794","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":[{"score":0.75590580701828,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Language-guided":[0],"Medical":[1],"Image":[2],"Segmentation":[3],"(LMIS)":[4],"has":[5],"shown":[6],"great":[7],"potential":[8],"to":[9,51,118,167],"improve":[10],"the":[11,61,105,129,206],"delineation":[12],"of":[13],"anatomical":[14],"structures":[15],"and":[16,33,48,60,114,123,127,174,179,208],"lesions":[17],"by":[18],"integrating":[19],"clinical":[20],"textual":[21,32],"information.":[22],"Existing":[23],"methods":[24,45],"generally":[25],"rely":[26],"on":[27,145,205],"either":[28],"implicit":[29],"interaction":[30],"between":[31,58],"visual":[34],"features":[35,122],"or":[36],"auxiliary":[37],"coarse-grained":[38],"supervision":[39,188],"for":[40,85,189],"cross-modal":[41,165,197],"alignment.":[42],"However,":[43],"these":[44],"lack":[46],"explicit":[47],"fine-grained":[49],"constraints":[50],"ensure":[52],"semantic":[53,83,90],"consistency,":[54],"causing":[55],"a":[56,74,141,151,158,180,195],"mismatch":[57],"language":[59],"segmentation":[62],"outputs.":[63],"To":[64],"address":[65],"this":[66],"issue,":[67],"we":[68,149],"propose":[69,150],"Text-as-Illumination":[70],"Retinex":[71],"Network":[72],"(TIRNet),":[73],"novel":[75],"Retinex-inspired":[76,106],"framework":[77],"that":[78,163,185,212],"treats":[79],"text":[80],"embeddings":[81],"as":[82],"illumination":[84,116,146,191],"feature":[86],"modulation,":[87],"thereby":[88],"improving":[89],"consistency":[91],"in":[92,170,176,217],"LMIS.":[93,218],"TIRNet":[94,213],"introduces":[95],"two":[96],"key":[97],"blocks":[98],"integrated":[99],"at":[100,199],"each":[101,200],"decoder":[102,201],"stage:":[103],"(1)":[104],"Text":[107],"Modulation":[108],"Block":[109,133],"(RTMB),":[110],"which":[111,135],"employs":[112],"positive":[113],"negative":[115,190],"maps":[117],"enhance":[119],"text-relevant":[120,171],"foreground":[121,172],"suppress":[124],"background":[125,177],"interference;":[126],"(2)":[128],"Consistent":[130],"Detail":[131],"Compensation":[132],"(CDCB),":[134],"selectively":[136],"recovers":[137],"high-frequency":[138],"details":[139],"via":[140],"consistency-gated":[142],"mechanism":[143],"conditioned":[144],"reliability.":[147],"Furthermore,":[148],"Multi-Scale":[152],"Illumination":[153],"Supervision":[154],"Loss":[155,161,183],"(MSIS-Loss),":[156],"comprising":[157],"Region-Grounded":[159],"Contrastive":[160],"(RGC-Loss)":[162],"enforces":[164],"similarity":[166],"be":[168],"concentrated":[169],"regions":[173],"suppressed":[175],"regions,":[178],"Background":[181],"Suppression":[182],"(BS-Loss)":[184],"provides":[186],"pixel-level":[187],"maps,":[192],"jointly":[193],"ensuring":[194],"precise":[196],"alignment":[198],"stage.":[202],"Extensive":[203],"experiments":[204],"MosMedData+":[207],"QaTa-COV19":[209],"datasets":[210],"demonstrate":[211],"achieves":[214],"state-of-the-art":[215],"performance":[216],"The":[219],"code":[220],"is":[221],"available":[222],"at:":[223],"https://github.com/anaanaa/TIRNet.":[224]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-30T00:00:00"}
