{"id":"https://openalex.org/W7092214635","doi":"https://doi.org/10.1109/access.2025.3622201","title":"Uncertainty Modelling for Tumour Cellularity Estimation in Histopathology Using Deep Learning","display_name":"Uncertainty Modelling for Tumour Cellularity Estimation in Histopathology Using Deep Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7092214635","doi":"https://doi.org/10.1109/access.2025.3622201"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3622201","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3622201","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3622201","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Riddhasree Bhattacharyya","orcid":"https://orcid.org/0009-0007-9824-2874"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Riddhasree Bhattacharyya","raw_affiliation_strings":["Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India"],"raw_orcid":"https://orcid.org/0009-0007-9824-2874","affiliations":[{"raw_affiliation_string":"Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India","institution_ids":["https://openalex.org/I6498739"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sushmita Mitra","orcid":"https://orcid.org/0000-0001-9285-1117"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sushmita Mitra","raw_affiliation_strings":["Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India"],"raw_orcid":"https://orcid.org/0000-0001-9285-1117","affiliations":[{"raw_affiliation_string":"Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India","institution_ids":["https://openalex.org/I6498739"]}]},{"author_position":"last","author":{"id":null,"display_name":"Sugata Banerji","orcid":"https://orcid.org/0000-0002-8278-0420"},"institutions":[{"id":"https://openalex.org/I162514690","display_name":"Lake Forest College","ror":"https://ror.org/04rmtzr09","country_code":"US","type":"education","lineage":["https://openalex.org/I162514690"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sugata Banerji","raw_affiliation_strings":["Department of Mathematics and Computer Science, Lake Forest College, Lake Forest, IL, USA"],"raw_orcid":"https://orcid.org/0000-0002-8278-0420","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, Lake Forest College, Lake Forest, IL, USA","institution_ids":["https://openalex.org/I162514690"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I6498739"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.0776,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.92392697,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"179922","last_page":"179931"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7979999780654907,"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":0.7979999780654907,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.05660000070929527,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.025800000876188278,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6740000247955322},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5878000259399414},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5674999952316284},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5189999938011169},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.5169000029563904},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.40869998931884766},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.33410000801086426},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.32179999351501465},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.3142000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7073000073432922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7031000256538391},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6740000247955322},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6650999784469604},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5878000259399414},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29190000891685486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3622201","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3622201","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8c3a014cec7644e99634320b47fbaa42","is_oa":true,"landing_page_url":"https://doaj.org/article/8c3a014cec7644e99634320b47fbaa42","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 179922-179931 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3622201","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3622201","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.40150079131126404,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Tumour":[0],"cellularity":[1,249],"(<italic":[2],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[3,63,80,116,169,208,216],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">TC</i>)":[4],"is":[5,102,122,131,151,164],"an":[6,214],"important":[7],"metric":[8],"used":[9],"in":[10,36,51,78,86,138,153,203],"the":[11,17,30,128,135,188,191,254],"cancer":[12],"treatment":[13,23],"journey,":[14],"from":[15],"monitoring":[16],"therapeutic":[18],"response":[19],"to":[20,90,104,124,157,193],"guiding":[21],"subsequent":[22],"decisions.":[24],"It":[25],"can":[26],"be":[27],"defined":[28],"as":[29,195],"area":[31],"occupied":[32],"by":[33,71,133,141],"malignant":[34],"cells":[35],"a":[37,98,119,147,154,196,206,240],"Region":[38],"of":[39,68,75,109,114,190,211,219,256],"Interest.":[40],"Although":[41],"deep":[42],"learning":[43],"(DL)-based":[44],"automated":[45,247],"systems":[46],"have":[47],"gained":[48],"considerable":[49],"momentum":[50],"medical":[52],"image":[53],"analysis,":[54],"there":[55],"are":[56],"still":[57],"challenges":[58],"associated":[59],"with":[60,178],"efficient":[61,112],"<italic":[62,79,115,168,207],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">TC</i>":[64,81,170],"computation.":[65,171],"The":[66,172],"scope":[67],"performance":[69,177],"improvement":[70],"comprehensively":[72],"addressing":[73],"issues":[74],"aleatoric":[76,126],"uncertainty":[77,85,130],"labels":[82],"and":[83,184,213,227,234,242,251],"epistemic":[84,129],"model":[87,137],"parameters":[88],"due":[89],"limited":[91,144],"data":[92],"remains":[93],"unexplored.":[94],"In":[95],"this":[96],"research,":[97],"novel":[99,148],"regression-based":[100],"framework":[101,156,192,238],"developed":[103],"address":[105],"these":[106],"two":[107,139],"types":[108],"uncertainties":[110],"for":[111,166,199,245,252],"computation":[113],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">TC</i>.":[117],"While":[118],"loss":[120],"function":[121],"introduced":[123],"handle":[125],"uncertainty,":[127],"handled":[132],"training":[134],"DL":[136],"stages":[140],"skilfully":[142],"utilizing":[143],"data.":[145],"First,":[146],"task-specific":[149],"pre-training":[150],"done":[152],"semi-supervised":[155],"capture":[158],"fine-grained":[159],"cell-level":[160],"features.":[161],"Then,":[162],"it":[163,221],"fine-tuned":[165],"effective":[167,176],"experimental":[173],"results":[174,186],"show":[175],"different":[179],"encoders,":[180],"demonstrating":[181],"robustness.":[182],"Quantitative":[183],"qualitative":[185],"demonstrate":[187],"potential":[189],"serve":[194],"robust":[197],"tool":[198],"clinical":[200],"decision":[201],"support":[202],"cancer.":[204],"Achieving":[205],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">PK</i>":[209],"score":[210,218],"0.95":[212],"R<sup":[215],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[217],"0.88,":[220],"outperforms":[222],"state-of-the-art":[223],"histopathology-specific":[224],"foundation":[225],"models":[226],"other":[228],"related":[229],"methods":[230],"w.r.t.":[231],"both":[232],"metrics":[233],"parameters.":[235],"Thus,":[236],"our":[237],"offers":[239],"reliable":[241],"scalable":[243],"solution":[244],"improved":[246],"tumour":[248],"estimation":[250],"reducing":[253],"workload":[255],"pathologists.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-17T00:00:00"}
