{"id":"https://openalex.org/W3196829339","doi":null,"title":"Histopathology Image Analysis and NLP for Digital Pathology","display_name":"Histopathology Image Analysis and NLP for Digital Pathology","publication_year":2021,"publication_date":"2021-08-27","ids":{"openalex":"https://openalex.org/W3196829339","mag":"3196829339"},"language":"en","primary_location":{"id":"pmh:oai:uwspace.uwaterloo.ca:10012/17305","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10012/17305","pdf_url":"https://uwspace.uwaterloo.ca/bitstreams/4ad9a409-0360-4043-8333-c5591e27df9c/download","source":{"id":"https://openalex.org/S4306401661","display_name":"UWSpace (University of Waterloo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I151746483","host_organization_name":"University of Waterloo","host_organization_lineage":["https://openalex.org/I151746483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Master Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://uwspace.uwaterloo.ca/bitstreams/4ad9a409-0360-4043-8333-c5591e27df9c/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011910857","display_name":"Aishwarya Allada Allada","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Aishwarya Allada Allada","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5011910857"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10862","display_name":"AI in cancer detection","score":0.9994999766349792,"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":0.9994999766349792,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/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/digital-pathology","display_name":"digital pathology","score":0.9309999942779541},{"id":"https://openalex.org/keywords/histopathological-image-analysis","display_name":"histopathology image analysis","score":0.8949999809265137},{"id":"https://openalex.org/keywords/whole-slide-images","display_name":"whole slide images","score":0.6710000038146973},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"computer-aided diagnosis","score":0.9110000133514404},{"id":"https://openalex.org/keywords/pathology-reports","display_name":"pathology reports","score":0.6140000224113464},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"natural language processing","score":0.6790000200271606},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.8629999756813049},{"id":"https://openalex.org/keywords/biobert","display_name":"BioBERT","score":0.8550000190734863},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.7609999775886536},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.8059999942779541},{"id":"https://openalex.org/keywords/multimodal-fusion","display_name":"multimodal fusion","score":0.8539999723434448},{"id":"https://openalex.org/keywords/cancer-classification","display_name":"cancer classification","score":0.625},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.7239999771118164}],"concepts":[{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.7654221653938293},{"id":"https://openalex.org/C544855455","wikidata":"https://www.wikidata.org/wiki/Q1070952","display_name":"Histopathology","level":2,"score":0.7646016478538513},{"id":"https://openalex.org/C2909954168","wikidata":"https://www.wikidata.org/wiki/Q860755","display_name":"Digital image analysis","level":2,"score":0.6026411056518555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5771316289901733},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5479032397270203},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.5171286463737488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4667990803718567},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.29783380031585693},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1898905336856842}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:uwspace.uwaterloo.ca:10012/17305","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10012/17305","pdf_url":"https://uwspace.uwaterloo.ca/bitstreams/4ad9a409-0360-4043-8333-c5591e27df9c/download","source":{"id":"https://openalex.org/S4306401661","display_name":"UWSpace (University of Waterloo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I151746483","host_organization_name":"University of Waterloo","host_organization_lineage":["https://openalex.org/I151746483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Master Thesis"},{"id":"mag:3196829339","doi":null,"is_oa":false,"landing_page_url":"https://libuwspaceprd02.uwaterloo.ca/handle/10012/17305","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:uwspace.uwaterloo.ca:10012/17305","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10012/17305","pdf_url":"https://uwspace.uwaterloo.ca/bitstreams/4ad9a409-0360-4043-8333-c5591e27df9c/download","source":{"id":"https://openalex.org/S4306401661","display_name":"UWSpace (University of Waterloo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I151746483","host_organization_name":"University of Waterloo","host_organization_lineage":["https://openalex.org/I151746483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Master Thesis"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6240000128746033,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3196829339.pdf","grobid_xml":"https://content.openalex.org/works/W3196829339.grobid-xml"},"referenced_works_count":101,"referenced_works":["https://openalex.org/W68438997","https://openalex.org/W95066055","https://openalex.org/W1486649854","https://openalex.org/W1514535095","https://openalex.org/W1535736950","https://openalex.org/W1614298861","https://openalex.org/W1686810756","https://openalex.org/W1816313093","https://openalex.org/W1869282115","https://openalex.org/W1880262756","https://openalex.org/W1902237438","https://openalex.org/W1965224467","https://openalex.org/W1986750896","https://openalex.org/W2009190245","https://openalex.org/W2010871781","https://openalex.org/W2012560661","https://openalex.org/W2040870580","https://openalex.org/W2064675550","https://openalex.org/W2070493638","https://openalex.org/W2072128103","https://openalex.org/W2076063813","https://openalex.org/W2076424959","https://openalex.org/W2087347434","https://openalex.org/W2104655020","https://openalex.org/W2107878631","https://openalex.org/W2117692326","https://openalex.org/W2120636855","https://openalex.org/W2129038679","https://openalex.org/W2129053638","https://openalex.org/W2129112648","https://openalex.org/W2130942839","https://openalex.org/W2132162500","https://openalex.org/W2133564696","https://openalex.org/W2133873291","https://openalex.org/W2135136011","https://openalex.org/W2148461049","https://openalex.org/W2149706766","https://openalex.org/W2150355110","https://openalex.org/W2150593711","https://openalex.org/W2153579005","https://openalex.org/W2155863877","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2442684275","https://openalex.org/W2504150216","https://openalex.org/W2525778437","https://openalex.org/W2563351168","https://openalex.org/W2580662672","https://openalex.org/W2587019100","https://openalex.org/W2606209624","https://openalex.org/W2618851150","https://openalex.org/W2683784395","https://openalex.org/W2752747624","https://openalex.org/W2774267535","https://openalex.org/W2775244861","https://openalex.org/W2782757030","https://openalex.org/W2785934082","https://openalex.org/W2786464815","https://openalex.org/W2787560479","https://openalex.org/W2794557536","https://openalex.org/W2885824038","https://openalex.org/W2895540242","https://openalex.org/W2896457183","https://openalex.org/W2905810301","https://openalex.org/W2907597838","https://openalex.org/W2913603921","https://openalex.org/W2914373263","https://openalex.org/W2921258119","https://openalex.org/W2925863688","https://openalex.org/W2949117887","https://openalex.org/W2949335953","https://openalex.org/W2949605076","https://openalex.org/W2949650786","https://openalex.org/W2950096400","https://openalex.org/W2950179405","https://openalex.org/W2951501516","https://openalex.org/W2951583185","https://openalex.org/W2951934944","https://openalex.org/W2951970475","https://openalex.org/W2952867657","https://openalex.org/W2962784628","https://openalex.org/W2962964995","https://openalex.org/W2963008443","https://openalex.org/W2963341956","https://openalex.org/W2964308564","https://openalex.org/W2965373594","https://openalex.org/W2981648168","https://openalex.org/W3006450303","https://openalex.org/W3009535750","https://openalex.org/W3017961061","https://openalex.org/W3018295606","https://openalex.org/W3023371261","https://openalex.org/W3031923721","https://openalex.org/W3089090082","https://openalex.org/W3089669417","https://openalex.org/W3090736188","https://openalex.org/W3099570996","https://openalex.org/W3101216323","https://openalex.org/W3112256016","https://openalex.org/W3157252883","https://openalex.org/W4394670654"],"related_works":["https://openalex.org/W3046154346","https://openalex.org/W4365455078","https://openalex.org/W3159976995","https://openalex.org/W4402134694","https://openalex.org/W4379537780","https://openalex.org/W3075287024","https://openalex.org/W3013166479","https://openalex.org/W4412921002","https://openalex.org/W3182003946","https://openalex.org/W2963513480"],"abstract_inverted_index":{"Information":[0],"technologies":[1],"based":[2],"on":[3],"ML":[4],"with":[5,51,90,328,343],"quantitative":[6,282],"imaging":[7],"and":[8,19,31,38,53,112,149,158,176,191,216,236,309,350,366,390],"texts":[9],"are":[10,87,97],"playing":[11],"an":[12,211,344,347,406],"essential":[13],"role,":[14],"particularly":[15],"in":[16,22,28,68,94,126,238,260,323,383],"general":[17],"medicine":[18],"oncology.":[20],"DL":[21,226,247,255],"particular":[23],"has":[24,209,256],"demonstrated":[25],"significant":[26,66],"breakthroughs":[27],"Computer":[29],"Vision":[30],"NLP":[32],"which":[33,120,170],"could":[34],"enhance":[35],"disease":[36,341],"detection":[37],"the":[39,54,69,109,132,163,167,172,181,188,196,203,231,261,270,273,313,324,329,360,363,367,375,378,381,384,401],"establishment":[40],"of":[41,49,57,71,84,108,136,174,178,180,198,206,234,272,298,315,331,334,362,369,377,380,386,408],"efficient":[42,217],"treatments.":[43],"Furthermore,":[44],"considering":[45],"a":[46,65,116,266,277,295,356],"large":[47],"number":[48],"people":[50],"cancer":[52,61],"substantial":[55],"volume":[56],"data":[58],"generated":[59],"during":[60],"treatment,":[62],"there":[63,208,264],"is":[64,103,113,121,171,241,265],"interest":[67],"use":[70,245],"AI":[72],"to":[73,139,154,194,202,230,244,268,275,339,358,397],"improve":[74],"oncologic":[75],"care.":[76],"\\n":[77,165,285],"\\nIn":[78],"digital":[79],"pathology,":[80],"high-resolution":[81],"microscope":[82],"images":[83,389],"tissue":[85,106],"samples":[86],"stored":[88,122],"along":[89],"written":[91,114],"medical":[92,137,152,262],"reports":[93,145,322,365,393],"databases":[95],"that":[96],"used":[98],"by":[99,151],"pathologists.":[100],"The":[101],"diagnosis":[102,175,224,253],"made":[104],"through":[105],"analysis":[107,184,297],"biopsy":[110],"sample":[111],"as":[115,123,222,303],"brief":[117],"unstructured":[118],"report":[119],"free":[124],"text":[125],"Electronic":[127],"Medical":[128],"Record":[129],"(EMR)systems.":[130],"For":[131],"transition":[133],"towards":[134],"digitization":[135],"records":[138],"achieve":[140],"its":[141],"maximum":[142],"benefits,":[143],"these":[144],"must":[146],"be":[147,398],"accessible":[148],"usable":[150],"practitioners":[153],"easily":[155],"understand":[156],"them":[157,160,338],"help":[159,330],"precisely":[161],"identify":[162,187],"disease.":[164],"\\nConcerning":[166],"histopathology":[168,388],"images,":[169],"basis":[173],"study":[177],"diseases":[179],"tissues,":[182],"image":[183,219],"helps":[185],"us":[186,193],"disease\u2019s":[189],"location":[190],"allows":[192],"classify":[195,337],"type":[197],"cancer.":[199],"Recently,":[200],"due":[201,229],"abundant":[204],"accumulation":[205],"WSIs,":[207,239,335],"been":[210],"increased":[212],"demand":[213],"for":[214,249,320],"effective":[215],"gigapixel":[218],"analysis,":[220],"such":[221,302],"computer-aided":[223,252],"using":[225,254],"techniques.":[227],"Also,":[228],"high":[232],"diversity":[233],"shapes":[235],"structures":[237],"it":[240],"not":[242],"possible":[243],"conventional":[246],"techniques":[248,319],"classification.":[250],"Though":[251],"good":[257],"prediction":[258,271],"accuracy,":[259],"domain,":[263],"need":[267],"explain":[269],"model":[274],"have":[276],"better":[278,399],"understanding":[279],"beyond":[280],"standard":[281],"performance":[283],"evaluation.":[284],"\\nThis":[286],"thesis":[287],"presents":[288],"three":[289],"different":[290],"findings.":[291],"Firstly,":[292],"I":[293,336,354,373],"provide":[294],"comparative":[296],"various":[299,316],"transformer":[300],"models":[301],"BioBERT,":[304,306],"Clinical":[305],"BioMed-RoBERTaand":[307],"TF-IDF":[308],"our":[310],"results":[311],"demonstrate":[312],"effectiveness":[314],"word":[317],"embedding":[318],"pathology":[321,364],"classification":[325,385,403],"task.":[326],"Secondly,":[327],"slide":[332],"labels":[333],"their":[340,370,391],"types,":[342],"architecture":[345],"having":[346],"attention":[348],"mechanism":[349],"instance-level":[351],"clustering.":[352],"Finally,":[353],"introduced":[355],"method":[357],"fuse":[359],"features":[361,368,382],"respective":[371,392],"images.":[372],"investigated":[374],"effect":[376],"combination":[379],"both":[387],"simultaneously.":[394],"This":[395],"proved":[396],"than":[400],"individual":[402],"tasks":[404],"achieving":[405],"accuracy":[407],"95.73%.":[409]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-09-13T00:00:00"}