{"id":"https://openalex.org/W4226409235","doi":"https://doi.org/10.1109/isbi52829.2022.9761615","title":"Learning with Less Labels in Digital Pathology Via Scribble Supervision from Natural Images","display_name":"Learning with Less Labels in Digital Pathology Via Scribble Supervision from Natural Images","publication_year":2022,"publication_date":"2022-03-28","ids":{"openalex":"https://openalex.org/W4226409235","doi":"https://doi.org/10.1109/isbi52829.2022.9761615"},"language":"en","primary_location":{"id":"doi:10.1109/isbi52829.2022.9761615","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761615","pdf_url":null,"source":{"id":"https://openalex.org/S4363605129","display_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040170901","display_name":"Eu Wern The","orcid":null},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]},{"id":"https://openalex.org/I4210127509","display_name":"Vector Institute","ror":"https://ror.org/03kqdja62","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210127509"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Eu Wern The","raw_affiliation_strings":["University of Guelph,School of Engineering,Canada","Vector Institute, Canada","School of Engineering, University of Guelph, Canada"],"affiliations":[{"raw_affiliation_string":"University of Guelph,School of Engineering,Canada","institution_ids":["https://openalex.org/I79817857"]},{"raw_affiliation_string":"Vector Institute, Canada","institution_ids":["https://openalex.org/I4210127509"]},{"raw_affiliation_string":"School of Engineering, University of Guelph, Canada","institution_ids":["https://openalex.org/I79817857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102922725","display_name":"Graham W. Taylor","orcid":"https://orcid.org/0000-0001-5867-3652"},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]},{"id":"https://openalex.org/I4210127509","display_name":"Vector Institute","ror":"https://ror.org/03kqdja62","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210127509"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Graham W. Taylor","raw_affiliation_strings":["University of Guelph,School of Engineering,Canada","Vector Institute, Canada","School of Engineering, University of Guelph, Canada"],"affiliations":[{"raw_affiliation_string":"University of Guelph,School of Engineering,Canada","institution_ids":["https://openalex.org/I79817857"]},{"raw_affiliation_string":"Vector Institute, Canada","institution_ids":["https://openalex.org/I4210127509"]},{"raw_affiliation_string":"School of Engineering, University of Guelph, Canada","institution_ids":["https://openalex.org/I79817857"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5040170901"],"corresponding_institution_ids":["https://openalex.org/I4210127509","https://openalex.org/I79817857"],"apc_list":null,"apc_paid":null,"fwci":0.1039,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.24121956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9944999814033508,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9919000267982483,"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/computer-science","display_name":"Computer science","score":0.8332004547119141},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7396678328514099},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6872336864471436},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6857690215110779},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.609532356262207},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5950667858123779},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.593891978263855},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5919846892356873},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5216087102890015},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.5089831352233887},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4635993540287018},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4317439794540405},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41432642936706543},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.399194598197937},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06518548727035522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8332004547119141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7396678328514099},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6872336864471436},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6857690215110779},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.609532356262207},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5950667858123779},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.593891978263855},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5919846892356873},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5216087102890015},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.5089831352233887},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4635993540287018},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4317439794540405},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41432642936706543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.399194598197937},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06518548727035522},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi52829.2022.9761615","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761615","pdf_url":null,"source":{"id":"https://openalex.org/S4363605129","display_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2108598243","https://openalex.org/W2186094539","https://openalex.org/W2194775991","https://openalex.org/W2337429362","https://openalex.org/W2412782625","https://openalex.org/W2435090885","https://openalex.org/W2570343428","https://openalex.org/W2806857275","https://openalex.org/W2963150697","https://openalex.org/W2963198662","https://openalex.org/W2963687373","https://openalex.org/W2963703197","https://openalex.org/W3026809105","https://openalex.org/W3089090082","https://openalex.org/W3177944450","https://openalex.org/W4295224294","https://openalex.org/W6639102338","https://openalex.org/W6686583229","https://openalex.org/W6703192839","https://openalex.org/W6725762072","https://openalex.org/W6750210775","https://openalex.org/W6783768408","https://openalex.org/W6798120220"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2625833328","https://openalex.org/W1533177136","https://openalex.org/W4380994516","https://openalex.org/W2921295405"],"abstract_inverted_index":{"A":[0],"critical":[1],"challenge":[2],"of":[3,64,72,103],"training":[4],"deep":[5],"learning":[6,31,47],"models":[7,105,123],"in":[8],"the":[9,15,33,39,70,101,129],"Digital":[10],"Pathology":[11],"(DP)":[12],"domain":[13,36,98],"is":[14,28,42,52,69],"high":[16],"annotation":[17,40],"cost":[18,41],"by":[19],"medical":[20],"experts.":[21],"One":[22,61],"way":[23],"to":[24,50,54,144],"tackle":[25],"this":[26],"issue":[27],"via":[29,57],"transfer":[30,46],"from":[32,48,79,96],"natural":[34],"image":[35],"(NI),":[37],"where":[38],"considerably":[43],"cheaper.":[44],"Cross-domain":[45],"NI":[49,97],"DP":[51,104],"shown":[53],"be":[55,77],"successful":[56],"class":[58,67],"labels":[59,68,81,87,95,127,137],"[1].":[60],"potential":[62],"weakness":[63],"relying":[65],"on":[66,106],"lack":[71],"spatial":[73,80],"information,":[74],"which":[75],"can":[76,99],"obtained":[78],"such":[82],"as":[83,133],"full":[84,134],"pixel-wise":[85,135],"segmentation":[86,136],"and":[88,115,142],"scribble":[89,94,126],"labels.":[90],"We":[91],"demonstrate":[92],"that":[93,122],"boost":[100,132],"performance":[102,131],"two":[107],"cancer":[108],"classification":[109],"datasets":[110],"(Patch":[111],"Camelyon":[112],"Breast":[113],"Cancer":[114,117],"Colorectal":[116],"dataset).":[118],"Furthermore,":[119],"we":[120],"show":[121],"trained":[124],"with":[125],"yield":[128],"same":[130],"despite":[138],"being":[139],"significantly":[140],"easier":[141],"faster":[143],"collect.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
