{"id":"https://openalex.org/W7135229934","doi":"https://doi.org/10.48550/arxiv.2603.11206","title":"Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction","display_name":"Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135229934","doi":"https://doi.org/10.48550/arxiv.2603.11206"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11206","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.11206","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124221142","display_name":"Jingxing Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Jingxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088060193","display_name":"Qingtao Pan","orcid":"https://orcid.org/0000-0001-9648-8618"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Qingtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129036089","display_name":"Xuchang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xuchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129015911","display_name":"Jiazhen Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Jiazhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5012622637","display_name":"Xinguo ZHUANG","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuang, Xinguo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T10862","display_name":"AI in cancer detection","score":0.7942000031471252,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7942000031471252,"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"}},{"id":"https://openalex.org/T11885","display_name":"MRI in cancer diagnosis","score":0.0860000029206276,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0272000003606081,"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/segmentation","display_name":"Segmentation","score":0.8463000059127808},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5370000004768372},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5041999816894531},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4878000020980835},{"id":"https://openalex.org/keywords/breast-tumor","display_name":"Breast tumor","score":0.413100004196167},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.41269999742507935},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.3716000020503998}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8463000059127808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7409999966621399},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6225000023841858},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5370000004768372},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5041999816894531},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4878000020980835},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43700000643730164},{"id":"https://openalex.org/C2986637895","wikidata":"https://www.wikidata.org/wiki/Q953865","display_name":"Breast tumor","level":4,"score":0.413100004196167},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.3716000020503998},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.35569998621940613},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C156201811","wikidata":"https://www.wikidata.org/wiki/Q5418360","display_name":"Evidential reasoning approach","level":4,"score":0.29019999504089355},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2703000009059906},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.2651999890804291},{"id":"https://openalex.org/C2777111374","wikidata":"https://www.wikidata.org/wiki/Q4959770","display_name":"Breast MRI","level":5,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11206","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.11206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11206","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.531179666519165}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Breast":[0,96],"cancer":[1,68],"is":[2,148],"one":[3],"of":[4,9,16,43,64,125,131,155,170,178,186],"the":[5,62,109,129,145,152,156,163,168,171,175,179,184,194],"most":[6],"common":[7],"causes":[8],"death":[10],"among":[11],"women":[12],"worldwide,":[13],"with":[14,101],"millions":[15],"fatalities":[17],"annually.":[18],"Magnetic":[19],"Resonance":[20],"Imaging":[21],"(MRI)":[22],"can":[23],"provide":[24],"various":[25],"sequences":[26],"for":[27,39,158],"characterizing":[28],"tumor":[29,50,58,83,197],"morphology":[30],"and":[31,34,41,69,72,105,119],"internal":[32],"patterns,":[33],"becomes":[35],"an":[36],"effective":[37],"tool":[38],"detection":[40],"diagnosis":[42],"breast":[44,196],"tumors.":[45],"However,":[46],"previous":[47],"deep-learning":[48],"based":[49],"segmentation":[51,84,88,153,172,176,191,198],"methods":[52],"have":[53],"limitations":[54],"in":[55,81,136,140],"accurately":[56],"locating":[57,137],"contours":[59],"due":[60],"to":[61,134,150,166],"challenge":[63],"low":[65,141],"contrast":[66,142],"between":[67,117],"normal":[70],"areas":[71,139],"blurred":[73,159],"boundaries.":[74,180],"Leveraging":[75],"text":[76,120,132],"prompt":[77],"information":[78,115],"holds":[79],"promise":[80],"ameliorating":[82],"effect":[85],"by":[86,91],"delineating":[87],"regions.":[89],"Inspired":[90],"this,":[92],"we":[93],"propose":[94],"text-guided":[95],"Tumor":[97],"Segmentation":[98],"model":[99,157],"(TextBCS)":[100],"stage-divided":[102,111],"vision-language":[103,112],"interaction":[104,113],"evidential":[106,146],"learning.":[107],"Specifically,":[108],"proposed":[110],"facilitates":[114],"mutual":[116],"visual":[118],"features":[121],"at":[122],"each":[123],"stage":[124],"down-sampling,":[126],"further":[127],"exerting":[128],"advantages":[130],"prompts":[133],"assist":[135],"lesion":[138],"scenarios.":[143],"Moreover,":[144],"learning":[147],"adopted":[149],"quantify":[151],"uncertainty":[154],"boundary.":[160],"It":[161],"utilizes":[162],"variational":[164],"Dirichlet":[165],"characterize":[167],"distribution":[169],"probabilities,":[173],"addressing":[174],"uncertainties":[177],"Extensive":[181],"experiments":[182],"validate":[183],"superiority":[185],"our":[187],"TextBCS":[188],"over":[189],"other":[190],"networks,":[192],"showcasing":[193],"best":[195],"performance":[199],"on":[200],"publicly":[201],"available":[202],"datasets.":[203]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-14T00:00:00"}
