{"id":"https://openalex.org/W7143031571","doi":"https://doi.org/10.1016/j.media.2026.104046","title":"OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations","display_name":"OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations","publication_year":2026,"publication_date":"2026-03-29","ids":{"openalex":"https://openalex.org/W7143031571","doi":"https://doi.org/10.1016/j.media.2026.104046","pmid":"https://pubmed.ncbi.nlm.nih.gov/41946232"},"language":"en","primary_location":{"id":"doi:10.1016/j.media.2026.104046","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.media.2026.104046","pdf_url":null,"source":{"id":"https://openalex.org/S116571295","display_name":"Medical Image Analysis","issn_l":"1361-8415","issn":["1361-8415","1361-8423","1361-8431"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Image Analysis","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.media.2026.104046","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013986627","display_name":"J. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]},{"id":"https://openalex.org/I4210119896","display_name":"King's College School","ror":"https://ror.org/02bbqcn27","country_code":"GB","type":"education","lineage":["https://openalex.org/I4210119896"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Junwen Wang","raw_affiliation_strings":["School of Biomedical Engineering & Imaging Sciences, King's College London, UK. Electronic address: junwen.wang@kcl.ac.uk"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering & Imaging Sciences, King's College London, UK. Electronic address: junwen.wang@kcl.ac.uk","institution_ids":["https://openalex.org/I4210119896","https://openalex.org/I183935753"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhonghao Wang","orcid":"https://orcid.org/0000-0002-6274-0173"},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhonghao Wang","raw_affiliation_strings":["School of Biomedical Engineering & Imaging Sciences, King's College London, UK"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering & Imaging Sciences, King's College London, UK","institution_ids":["https://openalex.org/I183935753"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029216408","display_name":"Oscar MacCormac","orcid":"https://orcid.org/0000-0002-2938-543X"},"institutions":[{"id":"https://openalex.org/I4210153400","display_name":"King's College Hospital","ror":"https://ror.org/044nptt90","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210111135","https://openalex.org/I4210153400"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Oscar MacCormac","raw_affiliation_strings":["School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Neurosurgery, King's College Hospital, London, UK"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Neurosurgery, King's College Hospital, London, UK","institution_ids":["https://openalex.org/I4210153400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055632531","display_name":"Jonathan Shapey","orcid":"https://orcid.org/0000-0003-0291-348X"},"institutions":[{"id":"https://openalex.org/I4210153400","display_name":"King's College Hospital","ror":"https://ror.org/044nptt90","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210111135","https://openalex.org/I4210153400"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jonathan Shapey","raw_affiliation_strings":["School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Neurosurgery, King's College Hospital, London, UK"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Neurosurgery, King's College Hospital, London, UK","institution_ids":["https://openalex.org/I4210153400"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028279799","display_name":"Tom Vercauteren","orcid":"https://orcid.org/0000-0003-1794-0456"},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tom Vercauteren","raw_affiliation_strings":["School of Biomedical Engineering & Imaging Sciences, King's College London, UK"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering & Imaging Sciences, King's College London, UK","institution_ids":["https://openalex.org/I183935753"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5013986627"],"corresponding_institution_ids":["https://openalex.org/I183935753","https://openalex.org/I4210119896"],"apc_list":{"value":3970,"currency":"USD","value_usd":3970},"apc_paid":{"value":3970,"currency":"USD","value_usd":3970},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82599215,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"111","issue":null,"first_page":"104046","last_page":"104046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.22930000722408295,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.22930000722408295,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.22759999334812164,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.16279999911785126,"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":0.7415000200271606},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5756999850273132},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5184999704360962},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5070000290870667},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48890000581741333},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4740000069141388},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46790000796318054},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4625000059604645},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44859999418258667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8019000291824341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7623000144958496},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7415000200271606},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5756999850273132},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5184999704360962},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5070000290870667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.40220001339912415},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.40130001306533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.media.2026.104046","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.media.2026.104046","pdf_url":null,"source":{"id":"https://openalex.org/S116571295","display_name":"Medical Image Analysis","issn_l":"1361-8415","issn":["1361-8415","1361-8423","1361-8431"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Image Analysis","raw_type":"journal-article"},{"id":"pmid:41946232","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41946232","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical image analysis","raw_type":null}],"best_oa_location":{"id":"doi:10.1016/j.media.2026.104046","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.media.2026.104046","pdf_url":null,"source":{"id":"https://openalex.org/S116571295","display_name":"Medical Image Analysis","issn_l":"1361-8415","issn":["1361-8415","1361-8423","1361-8431"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Image Analysis","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335374","display_name":"Invention for Innovation","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W184565980","https://openalex.org/W1901129140","https://openalex.org/W1909740415","https://openalex.org/W1966716734","https://openalex.org/W2098140880","https://openalex.org/W2608819578","https://openalex.org/W2679674811","https://openalex.org/W2732931556","https://openalex.org/W2763160469","https://openalex.org/W2793848630","https://openalex.org/W2871579310","https://openalex.org/W2887976372","https://openalex.org/W2899867782","https://openalex.org/W2905867642","https://openalex.org/W2921625280","https://openalex.org/W2964980777","https://openalex.org/W2971013993","https://openalex.org/W3044129898","https://openalex.org/W3097487475","https://openalex.org/W3104625815","https://openalex.org/W3107358227","https://openalex.org/W3127107873","https://openalex.org/W3127828503","https://openalex.org/W3165209762","https://openalex.org/W3202876959","https://openalex.org/W3204258583","https://openalex.org/W3204378754","https://openalex.org/W3214085799","https://openalex.org/W4225740823","https://openalex.org/W4293004747","https://openalex.org/W4304195579","https://openalex.org/W4308746318","https://openalex.org/W4315781049","https://openalex.org/W4323921292","https://openalex.org/W4360815779","https://openalex.org/W4381943716","https://openalex.org/W4385158618","https://openalex.org/W4387211220","https://openalex.org/W4390873131","https://openalex.org/W4392263308","https://openalex.org/W4399929809","https://openalex.org/W4401246897","https://openalex.org/W4402308616","https://openalex.org/W4403067359"],"related_works":[],"abstract_inverted_index":{"Despite":[0],"significant":[1],"advancements,":[2],"segmentation":[3,31,44,66,137,153],"based":[4],"on":[5,240],"deep":[6],"neural":[7],"networks":[8],"in":[9,24,135],"medical":[10,34,223],"and":[11,38,77,96,164,180,218,244,252],"surgical":[12,246],"imaging":[13,247],"faces":[14],"several":[15],"challenges,":[16],"two":[17],"of":[18,142,191,214,255],"which":[19,68],"we":[20,62,173,226],"aim":[21],"to":[22,54,132,150,165],"address":[23,166,211],"this":[25,60,145],"work.":[26],"First,":[27],"acquiring":[28],"complete":[29],"pixel-level":[30],"labels":[32],"for":[33,102,177,207,222],"images":[35],"is":[36,129,147],"time-consuming":[37],"requires":[39],"domain":[40],"expertise.":[41],"Second,":[42],"typical":[43],"pipelines":[45],"cannot":[46],"detect":[47],"out-of-distribution":[48],"(OOD)":[49],"pixels,":[50],"leaving":[51],"them":[52],"prone":[53],"spurious":[55],"outputs":[56],"during":[57],"deployment.":[58],"In":[59],"work,":[61],"propose":[63,227],"a":[64,159,200,228],"novel":[65],"approach":[67],"broadly":[69],"falls":[70],"within":[71,112],"the":[72,103,113,140,148,175,192,212,250],"positive-unlabelled":[73],"(PU)":[74],"learning":[75,162],"paradigm":[76],"exploits":[78],"tools":[79],"from":[80,88,92],"OOD":[81,169,193,203,216],"detection":[82,170,204],"techniques.":[83,171],"Our":[84,195],"framework":[85,196],"learns":[86],"only":[87],"sparsely":[89],"annotated":[90],"pixels":[91,118],"multiple":[93],"positive-only":[94,156],"classes":[95,122,188,235],"does":[97],"not":[98],"use":[99],"any":[100,185,202],"annotation":[101,179],"background":[104,134,178],"class.":[105],"These":[106],"multi-class":[107,152,242],"positive":[108,121],"annotations":[109,157],"naturally":[110],"fall":[111],"in-distribution":[114],"(ID)":[115],"set.":[116,194],"Unlabelled":[117],"may":[119],"contain":[120],"but":[123],"also":[124],"negative":[125],"ones,":[126],"including":[127],"what":[128],"typically":[130],"referred":[131],"as":[133,158,189,236],"standard":[136],"formulations.":[138],"To":[139,210],"best":[141],"our":[143,256],"knowledge,":[144],"work":[146],"first":[149],"formulate":[151],"with":[154,184],"sparse":[155],"pixel-wise":[160],"PU":[161],"problem":[163],"it":[167],"using":[168],"Here,":[172],"forgo":[174],"need":[176],"consider":[181],"these":[182],"together":[183],"other":[186],"unseen":[187],"part":[190],"can":[197],"integrate,":[198],"at":[199],"pixel-level,":[201],"approaches":[205],"designed":[206],"classification":[208],"tasks.":[209],"lack":[213],"existing":[215],"datasets":[217,248],"established":[219],"evaluation":[220],"metric":[221],"image":[224],"segmentation,":[225],"cross-validation":[229],"strategy":[230],"that":[231],"treats":[232],"held-out":[233],"labelled":[234],"OOD.":[237],"Extensive":[238],"experiments":[239],"both":[241],"hyperspectral":[243],"RGB":[245],"demonstrate":[249],"robustness":[251],"generalisation":[253],"capability":[254],"proposed":[257],"framework.":[258]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-03-30T00:00:00"}
