{"id":"https://openalex.org/W7133294577","doi":"https://doi.org/10.48550/arxiv.2603.01115","title":"GuiDINO: Rethinking Vision Foundation Model in Medical Image Segmentation","display_name":"GuiDINO: Rethinking Vision Foundation Model in Medical Image Segmentation","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133294577","doi":"https://doi.org/10.48550/arxiv.2603.01115"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01115","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01115","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027453427","display_name":"Zhuonan Liang","orcid":"https://orcid.org/0000-0002-4459-0861"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liang, Zhuonan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127970406","display_name":"Wei Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127898133","display_name":"Jie Gan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gan, Jie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111128651","display_name":"Yaxuan Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yaxuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127916890","display_name":"Runnan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Runnan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047619963","display_name":"Hang Chang","orcid":"https://orcid.org/0000-0002-3773-6818"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Hang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127893710","display_name":"Weidong Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Weidong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5027453427"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6169999837875366,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6169999837875366,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.0640999972820282,"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":0.0421999990940094,"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":0.7379000186920166},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.620199978351593},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5235000252723694},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.4973999857902527},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4618000090122223},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4406000077724457},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39410001039505005},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.36730000376701355}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7379000186920166},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725999712944031},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.620199978351593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6064000129699707},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5235000252723694},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5081999897956848},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4618000090122223},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4406000077724457},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39410001039505005},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3248000144958496},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.32109999656677246},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2912999987602234},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.290800005197525},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2619999945163727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01115","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01115","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":"article"},"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":{"Foundation":[0],"vision":[1],"models":[2,16,167],"are":[3],"increasingly":[4],"adopted":[5],"in":[6,80],"medical":[7,19,96,140,171],"image":[8,20],"analysis.":[9],"Due":[10],"to":[11,40,112,122,157],"domain":[12],"shift,":[13],"these":[14],"pretrained":[15],"misalign":[17],"with":[18],"segmentation":[21,82,148],"needs":[22],"without":[23],"being":[24],"fully":[25],"fine-tuned":[26],"or":[27],"lightly":[28],"adapted.":[29],"We":[30],"introduce":[31],"GuiDINO,":[32],"a":[33,43,61,66,102,118,154,161],"framework":[34],"that":[35,107],"repositions":[36],"native":[37],"foundation":[38,166],"model":[39],"acting":[41],"as":[42],"visual":[44,52],"guidance":[45],"generator":[46],"for":[47],"downstream":[48],"segmentation.":[49],"GuiDINO":[50,126,145],"extracts":[51],"feature":[53,78],"representation":[54],"from":[55],"DINOv3":[56,135],"and":[57,93,142,150,159],"converts":[58],"them":[59],"into":[60],"spatial":[62],"guide":[63,75,103,110,136],"mask":[64,76,111],"via":[65],"lightweight":[67],"TokenBook":[68],"mechanism,":[69],"which":[70],"aggregates":[71],"token-prototype":[72],"similarities.":[73],"This":[74],"gates":[77],"activations":[79],"multiple":[81],"backbones,":[83],"thereby":[84],"injecting":[85],"foundation-model":[86],"priors":[87],"while":[88],"preserving":[89],"the":[90,109,134],"inductive":[91],"biases":[92],"efficiency":[94],"of":[95],"dedicated":[97],"architectures.":[98],"Training":[99],"relies":[100],"on":[101,133,164],"supervision":[104],"objective":[105],"loss":[106,121],"aligns":[108],"ground-truth":[113],"regions,":[114],"optionally":[115],"augmented":[116],"by":[117],"boundary-focused":[119],"hinge":[120],"sharpen":[123],"fine":[124],"structures.":[125],"also":[127],"supports":[128],"parameter-efficient":[129],"adaptation":[130],"through":[131],"LoRA":[132],"backbone.":[137],"Across":[138],"diverse":[139],"datasets":[141],"nnUNet-style":[143],"inference,":[144],"consistently":[146],"improves":[147],"quality":[149],"boundary":[151],"robustness,":[152],"suggesting":[153],"practical":[155],"alternative":[156],"fine-tuning":[158],"offering":[160],"new":[162],"perspective":[163],"how":[165],"can":[168],"best":[169],"serve":[170],"vision.":[172],"Code":[173],"is":[174],"available":[175],"at":[176],"https://github.com/Hi-FishU/GuiDINO":[177]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}
