{"id":"https://openalex.org/W7167584758","doi":"https://doi.org/10.48550/arxiv.2607.03058","title":"RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation","display_name":"RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation","publication_year":2026,"publication_date":"2026-07-03","ids":{"openalex":"https://openalex.org/W7167584758","doi":"https://doi.org/10.48550/arxiv.2607.03058"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.03058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.03058","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.2607.03058","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112328878","display_name":"Jiachi Zhang","orcid":"https://orcid.org/0000-0002-9887-7032"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiachi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104036081","display_name":"Z Z Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhuoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140201581","display_name":"Wenqi Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Wenqi","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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.991599977016449,"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"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.991599977016449,"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":0.0010000000474974513,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.0008999999845400453,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.66839998960495},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.609000027179718},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5486999750137329},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5220999717712402},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4702000021934509},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45010000467300415},{"id":"https://openalex.org/keywords/host","display_name":"Host (biology)","score":0.4404999911785126},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4251999855041504}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6973000168800354},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.66839998960495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.61080002784729},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5486999750137329},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5220999717712402},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4702000021934509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45010000467300415},{"id":"https://openalex.org/C126831891","wikidata":"https://www.wikidata.org/wiki/Q221673","display_name":"Host (biology)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4383000135421753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C4924752","wikidata":"https://www.wikidata.org/wiki/Q184148","display_name":"Plug-in","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C2778435480","wikidata":"https://www.wikidata.org/wiki/Q840387","display_name":"Colonoscopy","level":4,"score":0.35249999165534973},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35190001130104065},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34380000829696655},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2793999910354614},{"id":"https://openalex.org/C64802038","wikidata":"https://www.wikidata.org/wiki/Q131012","display_name":"Occam's razor","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.03058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.03058","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.2607.03058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.03058","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":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4462534487247467}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Post-refinement":[0],"can":[1],"improve":[2],"colonoscopy":[3,52],"segmentation":[4],"after":[5],"host":[6,29,102],"inference,":[7],"but":[8],"many":[9],"designs":[10],"still":[11],"rely":[12],"on":[13,167],"extra":[14],"correction":[15],"heads":[16],"or":[17,23],"multi-stage":[18],"pipelines":[19],"with":[20,36],"non-negligible":[21],"parameter":[22],"computational":[24],"cost.":[25],"For":[26],"polyp":[27],"segmentation,":[28],"predictions":[30],"are":[31],"often":[32],"already":[33],"reasonable":[34],"globally,":[35],"remaining":[37],"errors":[38,49],"clustered":[39],"around":[40],"ambiguous":[41],"boundaries":[42],"and":[43,61,110,116,132,147,151,161,170],"difficult":[44],"local":[45,73],"structures.":[46],"These":[47],"residual":[48,119],"matter":[50],"in":[51,75,95],"images":[53],"because":[54],"useful":[55],"masks":[56],"need":[57],"correct":[58],"lesion":[59],"coverage":[60],"clean":[62],"contour":[63],"delineation":[64],"across":[65],"subtle":[66],"mucosal":[67],"transitions.":[68],"This":[69],"setting":[70],"favors":[71],"selective":[72],"repair":[74],"prediction":[76,96,111],"space":[77],"over":[78],"reprocessing":[79],"the":[80,136],"entire":[81],"mask.":[82],"We":[83],"therefore":[84],"propose":[85],"RIGS-Refiner,":[86],"a":[87,100,122,171],"lightweight":[88,107],"post-refinement":[89,177],"plugin":[90],"for":[91,140,145,153],"risk-guided":[92,114],"recursive":[93,124],"refinement":[94,137],"space.":[97],"Starting":[98],"from":[99],"frozen":[101,157],"anchor":[103],"prediction,":[104],"RIGS-Refiner":[105],"extracts":[106],"image":[108],"priors":[109],"cues,":[112],"applies":[113],"update,":[115],"writes":[117],"back":[118],"corrections":[120],"through":[121],"shared":[123],"cell.":[125],"The":[126],"module":[127],"adds":[128],"only":[129],"+519":[130],"parameters":[131],"+0.631":[133],"GFLOPs,":[134],"keeping":[135],"path":[138],"compact":[139],"deployment.":[141],"Experiments":[142],"use":[143],"Kvasir-SEG":[144],"training":[146],"Kvasir,":[148],"ClinicDB,":[149],"ColonDB,":[150],"ETIS":[152],"evaluation":[154],"under":[155],"two":[156],"hosts,":[158],"namely":[159],"PraNet":[160],"SegFormer-B0.":[162],"Results":[163],"show":[164],"consistent":[165],"gains":[166],"both":[168],"hosts":[169],"favorable":[172],"efficiency-accuracy":[173],"trade-off":[174],"against":[175],"representative":[176],"methods.":[178],"Code":[179],"is":[180],"available":[181],"at":[182],"https://github.com/tyui99/RIGS-Refiner.":[183]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}