{"id":"https://openalex.org/W4293499111","doi":"https://doi.org/10.1109/tmi.2022.3201981","title":"Knowledge-Based Representation Learning for Nucleus Instance Classification From Histopathological Images","display_name":"Knowledge-Based Representation Learning for Nucleus Instance Classification From Histopathological Images","publication_year":2022,"publication_date":"2022-08-29","ids":{"openalex":"https://openalex.org/W4293499111","doi":"https://doi.org/10.1109/tmi.2022.3201981","pmid":"https://pubmed.ncbi.nlm.nih.gov/36037453"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2022.3201981","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3201981","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/journal_contribution/Knowledge-Based_Representation_Learning_for_Nucleus_Instance_Classification_From_Histopathological_Images/22092026","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100443651","display_name":"Wenhua Zhang","orcid":"https://orcid.org/0000-0001-5411-0684"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Wenhua Zhang","raw_affiliation_strings":["Tencent, Shenzhen, China","Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR"],"raw_orcid":"https://orcid.org/0000-0001-5411-0684","affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433042","display_name":"Jun Zhang","orcid":"https://orcid.org/0000-0001-5579-7094"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Zhang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5579-7094","affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057464765","display_name":"Sen Yang","orcid":"https://orcid.org/0000-0002-0639-4122"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Yang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061309609","display_name":"Xiyue Wang","orcid":"https://orcid.org/0000-0002-3597-9090"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiyue Wang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3597-9090","affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100748707","display_name":"Wei Yang","orcid":"https://orcid.org/0000-0002-6488-2546"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Yang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9548-1227","affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100668416","display_name":"Wenping Wang","orcid":"https://orcid.org/0000-0002-2284-3952"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenping Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100611801","display_name":"Xiao Han","orcid":"https://orcid.org/0000-0002-5151-6547"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Han","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3484,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64861205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"41","issue":"12","first_page":"3939","last_page":"3951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9958000183105469,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9480000138282776,"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/computer-science","display_name":"Computer science","score":0.8050318956375122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7188538908958435},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6362738609313965},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6262929439544678},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.625542163848877},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5326929092407227},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49586591124534607},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4736955165863037},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.45542967319488525},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4412824213504791},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4336458146572113},{"id":"https://openalex.org/keywords/nucleus","display_name":"Nucleus","score":0.4135923385620117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3664690852165222}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8050318956375122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7188538908958435},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6362738609313965},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6262929439544678},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.625542163848877},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5326929092407227},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49586591124534607},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4736955165863037},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.45542967319488525},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4412824213504791},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4336458146572113},{"id":"https://openalex.org/C2780723820","wikidata":"https://www.wikidata.org/wiki/Q1934178","display_name":"Nucleus","level":2,"score":0.4135923385620117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3664690852165222},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C95444343","wikidata":"https://www.wikidata.org/wiki/Q7141","display_name":"Cell biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D002467","descriptor_name":"Cell Nucleus","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":true},{"descriptor_ui":"D002467","descriptor_name":"Cell Nucleus","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":true},{"descriptor_ui":"D002467","descriptor_name":"Cell Nucleus","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":true},{"descriptor_ui":"D013194","descriptor_name":"Staining and Labeling","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013194","descriptor_name":"Staining and Labeling","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013194","descriptor_name":"Staining and Labeling","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1109/tmi.2022.3201981","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3201981","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:36037453","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36037453","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":"IEEE transactions on medical imaging","raw_type":null},{"id":"pmh:oai:figshare.com:article/22092026","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Knowledge-Based_Representation_Learning_for_Nucleus_Instance_Classification_From_Histopathological_Images/22092026","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/22092026","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Knowledge-Based_Representation_Learning_for_Nucleus_Instance_Classification_From_Histopathological_Images/22092026","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1607147148","https://openalex.org/W2051866343","https://openalex.org/W2073943895","https://openalex.org/W2096733369","https://openalex.org/W2100125385","https://openalex.org/W2101234009","https://openalex.org/W2194775991","https://openalex.org/W2312404985","https://openalex.org/W2511945703","https://openalex.org/W2606377603","https://openalex.org/W2734352056","https://openalex.org/W2767410506","https://openalex.org/W2790432037","https://openalex.org/W2793939854","https://openalex.org/W2894176037","https://openalex.org/W2921092847","https://openalex.org/W2944093511","https://openalex.org/W2949983948","https://openalex.org/W2955737766","https://openalex.org/W2962585490","https://openalex.org/W2962804068","https://openalex.org/W2963026686","https://openalex.org/W2963150697","https://openalex.org/W2963840026","https://openalex.org/W2964282006","https://openalex.org/W2969278648","https://openalex.org/W2974825848","https://openalex.org/W3000726258","https://openalex.org/W3005680577","https://openalex.org/W3009561768","https://openalex.org/W3012315701","https://openalex.org/W3013627541","https://openalex.org/W3035060554","https://openalex.org/W3035524453","https://openalex.org/W3040784645","https://openalex.org/W3089090082","https://openalex.org/W3097217077","https://openalex.org/W3106250896","https://openalex.org/W3136850285","https://openalex.org/W3171007011","https://openalex.org/W3173151551","https://openalex.org/W3210550184","https://openalex.org/W4286821862","https://openalex.org/W4286849947","https://openalex.org/W4289300435","https://openalex.org/W4293584584","https://openalex.org/W6674970907","https://openalex.org/W6675354045","https://openalex.org/W6740522475","https://openalex.org/W6750227808","https://openalex.org/W6764232654","https://openalex.org/W6774314701","https://openalex.org/W6774670964","https://openalex.org/W6775298725","https://openalex.org/W6775769912","https://openalex.org/W6779326418","https://openalex.org/W6783768408","https://openalex.org/W6785652829","https://openalex.org/W6786614245","https://openalex.org/W6790534210","https://openalex.org/W6797132756","https://openalex.org/W6803376173","https://openalex.org/W6804050801"],"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/W2556260348"],"abstract_inverted_index":{"The":[0,79,218],"classification":[1,24,60,154,241],"of":[2,16,69,94,184,193,204,231],"nuclei":[3],"in":[4,12,239],"H&E-stained":[5,194],"histopathological":[6],"images":[7,196],"is":[8,49],"a":[9,64,70,75,117,134,143,190,214,236],"fundamental":[10],"step":[11],"the":[13,26,31,42,59,87,92,95,167,172,229,245],"quantitative":[14],"analysis":[15],"digital":[17],"pathology.":[18],"Most":[19],"existing":[20],"methods":[21],"employ":[22],"multi-class":[23],"on":[25,121,139,221],"detected":[27],"nucleus":[28,46,123,198,240],"instances,":[29],"while":[30,86],"annotation":[32],"scale":[33],"greatly":[34],"limits":[35],"their":[36],"performance.":[37,181],"Moreover,":[38],"they":[39],"often":[40],"downplay":[41],"contextual":[43,56,84],"information":[44,57],"surrounding":[45],"instances":[47,124],"that":[48],"critical":[50],"for":[51,114],"classification.":[52],"To":[53],"explicitly":[54],"provide":[55],"to":[58,97,150,177],"model,":[61],"we":[62,110,187],"design":[63],"new":[65,191],"structured":[66,107],"input":[67,108],"consisting":[68],"content-rich":[71],"image":[72,80],"patch":[73,81,211],"and":[74,100,128,171],"target":[76,88],"instance":[77,89,96,199],"mask.":[78],"provides":[82],"rich":[83],"information,":[85],"mask":[90],"indicates":[91],"location":[93],"be":[98],"classified":[99],"emphasizes":[101],"its":[102,180],"shape.":[103],"Benefiting":[104],"from":[105,213],"our":[106,225],"format,":[109],"propose":[111],"Structured":[112],"Triplet":[113],"representation":[115],"learning,":[116],"triplet":[118],"learning":[119,169,175],"framework":[120,141,226],"unlabelled":[122],"with":[125,142,156,197,224,244],"customized":[126],"positive":[127],"negative":[129],"sampling":[130],"strategies.":[131],"We":[132,160,234],"pre-train":[133],"feature":[135],"extraction":[136],"model":[137,155,219],"based":[138],"this":[140,185,222],"large-scale":[144],"unlabeled":[145],"dataset,":[146],"making":[147],"it":[148],"possible":[149],"train":[151],"an":[152],"effective":[153],"limited":[157],"annotated":[158],"data.":[159],"also":[161],"add":[162],"two":[163],"auxiliary":[164],"branches,":[165],"namely":[166],"attribute":[168],"branch":[170],"conventional":[173],"self-supervised":[174],"branch,":[176],"further":[178],"improve":[179],"As":[182],"part":[183],"work,":[186],"will":[188],"release":[189],"dataset":[192,223],"pathology":[195],"masks,":[200],"containing":[201],"20,187":[202],"patches":[203],"size":[205],"1024":[206],"\u00d71024":[207],",":[208],"where":[209],"each":[210],"comes":[212],"different":[215],"whole-slide":[216],"image.":[217],"pre-trained":[220],"significantly":[227],"reduces":[228],"burden":[230],"extensive":[232],"labeling.":[233],"show":[235],"substantial":[237],"improvement":[238],"accuracy":[242],"compared":[243],"state-of-the-art":[246],"methods.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}