{"id":"https://openalex.org/W2995104607","doi":"https://doi.org/10.48550/arxiv.1905.06820","title":"Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification","display_name":"Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification","publication_year":2019,"publication_date":"2019-05-16","ids":{"openalex":"https://openalex.org/W2995104607","doi":"https://doi.org/10.48550/arxiv.1905.06820","mag":"2995104607"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1905.06820","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.06820","pdf_url":"https://arxiv.org/pdf/1905.06820","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.06820","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061784316","display_name":"Koen Dercksen","orcid":"https://orcid.org/0000-0003-2571-9102","observed_orcids":["https://orcid.org/0000-0003-2571-9102"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dercksen, Koen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027255199","display_name":"Wouter Bulten","orcid":"https://orcid.org/0000-0002-6129-5039","observed_orcids":["https://orcid.org/0000-0002-6129-5039"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bulten, Wouter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055697525","display_name":"Geert J. S. Litjens","orcid":"https://orcid.org/0000-0003-1554-1291","observed_orcids":["https://orcid.org/0000-0003-1554-1291"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Litjens, Geert","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":true,"cited_by_count":7,"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/T10862","display_name":"AI in cancer detection","score":0.9990000128746033,"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":0.9990000128746033,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.00039999998989515007,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":9.999999747378752e-05,"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/limited-labeled-data","display_name":"limited labeled data","score":0.9879999756813049},{"id":"https://openalex.org/keywords/prostate-cancer-classification","display_name":"prostate cancer classification","score":0.8309999704360962},{"id":"https://openalex.org/keywords/computational-pathology","display_name":"computational pathology","score":0.7820000052452087},{"id":"https://openalex.org/keywords/unsupervised-deep-learning","display_name":"unsupervised deep learning","score":0.7450000047683716},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.8360000252723694},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8679999709129333},{"id":"https://openalex.org/keywords/h-e-stained-images","display_name":"H&E-stained images","score":0.6629999876022339},{"id":"https://openalex.org/keywords/immunohistochemistry","display_name":"immunohistochemistry","score":0.847000002861023}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8243862390518188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7750962972640991},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.6874566674232483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6503512263298035},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5948489308357239},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5615904927253723},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5585440397262573},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5455223917961121},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.48879095911979675},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4713689088821411},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4671477675437927},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.45558059215545654},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4386247992515564},{"id":"https://openalex.org/C2780192828","wikidata":"https://www.wikidata.org/wiki/Q181257","display_name":"Prostate cancer","level":3,"score":0.41287341713905334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36025166511535645},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20026683807373047},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.17404723167419434},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1905.06820","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.06820","pdf_url":"https://arxiv.org/pdf/1905.06820","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1905.06820","doi":"https://doi.org/10.48550/arxiv.1905.06820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1905.06820","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,"listed_in":[],"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"},{"id":"mag:2995104607","doi":null,"is_oa":false,"landing_page_url":"https://openreview.net/pdf?id=SJlq_10N94","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1905.06820","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.06820","pdf_url":"https://arxiv.org/pdf/1905.06820","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2995104607.pdf","grobid_xml":"https://content.openalex.org/works/W2995104607.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W2039758605","https://openalex.org/W2338271170","https://openalex.org/W2592929672","https://openalex.org/W2604440528"],"related_works":["https://openalex.org/W3214564172","https://openalex.org/W7039178123","https://openalex.org/W4309852369","https://openalex.org/W4394645985","https://openalex.org/W7018326111","https://openalex.org/W7125475889","https://openalex.org/W3048659280","https://openalex.org/W7162429516","https://openalex.org/W3018295606","https://openalex.org/W4388503456"],"abstract_inverted_index":{"Large":[0],"amounts":[1],"of":[2,30],"unlabelled":[3],"data":[4,15,40,108],"are":[5,41,45,98],"commonplace":[6],"for":[7],"many":[8],"applications":[9],"in":[10,20,112],"computational":[11],"pathology,":[12],"whereas":[13],"labelled":[14,39],"is":[16],"often":[17],"expensive,":[18],"both":[19],"time":[21],"and":[22,32,62,72],"cost,":[23],"to":[24,78],"acquire.":[25],"We":[26,66],"investigate":[27],"the":[28],"performance":[29,113],"unsupervised":[31],"supervised":[33,64,93],"deep":[34],"learning":[35,94],"methods":[36,44,69,88],"when":[37,95],"few":[38,96],"available.":[42,99],"Three":[43],"compared:":[46],"clustering":[47],"autoencoder":[48,60],"latent":[49],"vectors":[50],"(unsupervised),":[51],"a":[52,58,63],"single":[53],"layer":[54],"classifier":[55],"combined":[56],"with":[57],"pre-trained":[59],"(semi-supervised),":[61],"CNN.":[65],"apply":[67],"these":[68],"on":[70],"hematoxylin":[71],"eosin":[73],"(H&E)":[74],"stained":[75,107],"prostatectomy":[76],"images":[77],"classify":[79],"tumour":[80],"versus":[81],"non-tumour":[82],"tissue.":[83],"Results":[84],"show":[85,102],"that":[86,103],"semi-/unsupervised":[87],"have":[89],"an":[90,110],"advantage":[91],"over":[92,114],"labels":[97],"Additionally,":[100],"we":[101],"incorporating":[104],"immunohistochemistry":[105],"(IHC)":[106],"provides":[109],"increase":[111],"only":[115],"using":[116],"H&E.":[117]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-12-26T00:00:00"}
