{"id":"https://openalex.org/W7159623866","doi":"https://doi.org/10.48550/arxiv.2604.28095","title":"UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation","display_name":"UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159623866","doi":"https://doi.org/10.48550/arxiv.2604.28095"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.28095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.28095","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":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.2604.28095","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134982927","display_name":"Shuokun Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Shuokun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134983972","display_name":"Jinghao Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Jinghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134962518","display_name":"Kun Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Kun","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/T10036","display_name":"Advanced Neural Network Applications","score":0.3264000117778778,"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":0.3264000117778778,"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.2741999924182892,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10862","display_name":"AI in cancer detection","score":0.19329999387264252,"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/hypergraph","display_name":"Hypergraph","score":0.9262999892234802},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6970999836921692},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5992000102996826},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5841000080108643},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5094000101089478},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4408000111579895}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.9262999892234802},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6970999836921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6061999797821045},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5992000102996826},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5841000080108643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5713000297546387},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5094000101089478},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4408000111579895},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36329999566078186},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35580000281333923},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.259799987077713}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.28095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.28095","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":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.2604.28095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.28095","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6246190071105957,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"lesion":[1],"segmentation":[2],"is":[3,141],"crucial":[4],"for":[5,80],"clinical":[6],"diagnosis":[7],"and":[8,17,82,116,123],"treatment":[9],"planning.":[10],"However,":[11],"lesions":[12],"often":[13],"resemble":[14],"surrounding":[15],"tissues":[16],"exhibit":[18],"ill-defined":[19],"boundaries,":[20],"leading":[21],"to":[22,76,106],"unstable":[23],"predictions":[24],"in":[25,126],"boundary/transition":[26],"regions.":[27,128],"Moreover,":[28],"small-lesion":[29],"cues":[30],"can":[31],"be":[32],"diluted":[33],"by":[34],"multi-scale":[35],"feature":[36],"extraction,":[37],"causing":[38],"under-":[39],"or":[40],"over-segmentation.":[41],"To":[42],"address":[43],"these":[44],"challenges,":[45],"we":[46,55,87],"propose":[47],"an":[48,57,89,97],"Uncertainty-Aware":[49],"Hypergraph":[50,91],"Refinement":[51,92],"Network":[52],"(UHR-Net).":[53],"First,":[54],"introduce":[56],"Uncertainty-Oriented":[58],"Instance":[59],"Contrastive":[60],"(UO-IC)":[61],"pretraining":[62],"strategy":[63],"that":[64],"couples":[65],"geometry-aware":[66],"copy-paste":[67],"augmentation":[68],"with":[69],"hard-negative":[70],"mining":[71],"of":[72],"lesion-like":[73],"background":[74,117],"regions":[75],"improve":[77],"instance-level":[78],"discrimination":[79],"small":[81],"visually":[83],"ambiguous":[84,127],"lesions.":[85],"Second,":[86],"design":[88],"Uncertainty-Guided":[90],"(UGHR)":[93],"block,":[94],"which":[95],"derives":[96],"entropy-based":[98],"uncertainty":[99],"map":[100,105],"from":[101],"a":[102],"coarse":[103],"probability":[104],"guide":[107],"hypergraph":[108],"refinement.":[109],"By":[110],"splitting":[111],"hyperedge":[112],"prototypes":[113],"into":[114],"foreground":[115],"groups,":[118],"UGHR":[119],"decouples":[120],"higher-order":[121],"interactions":[122],"improves":[124],"refinement":[125],"Experiments":[129],"on":[130],"five":[131],"public":[132],"benchmarks":[133],"demonstrate":[134],"consistent":[135],"gains":[136],"over":[137],"strong":[138],"baselines.":[139],"Code":[140],"available":[142],"at:":[143],"https://github.com/CUGfreshman/UHR-Net.":[144]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-02T00:00:00"}