{"id":"https://openalex.org/W4416674841","doi":"https://doi.org/10.1088/2632-2153/ae1f5c","title":"Deep learning-driven glioblastoma diagnosis from histopathological images via single-cell segmentation and morphological analysis","display_name":"Deep learning-driven glioblastoma diagnosis from histopathological images via single-cell segmentation and morphological analysis","publication_year":2025,"publication_date":"2025-11-13","ids":{"openalex":"https://openalex.org/W4416674841","doi":"https://doi.org/10.1088/2632-2153/ae1f5c"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/ae1f5c","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ae1f5c","pdf_url":null,"source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1088/2632-2153/ae1f5c","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059637395","display_name":"Gonzalo Rosa","orcid":"https://orcid.org/0000-0002-3236-1236"},"institutions":[{"id":"https://openalex.org/I88060688","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02","country_code":"ES","type":"education","lineage":["https://openalex.org/I88060688"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Gonzalo Rosa-Olmeda","raw_affiliation_strings":["Universidad Polit\u00e9cnica de Madrid, Avenida Ramiro de Maeztu 7, Madrid, 28040, SPAIN"],"raw_orcid":"https://orcid.org/0000-0002-3236-1236","affiliations":[{"raw_affiliation_string":"Universidad Polit\u00e9cnica de Madrid, Avenida Ramiro de Maeztu 7, Madrid, 28040, SPAIN","institution_ids":["https://openalex.org/I88060688"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091922946","display_name":"Sara Hiller-Vallina","orcid":"https://orcid.org/0000-0001-5973-9991"},"institutions":[{"id":"https://openalex.org/I4210086614","display_name":"Research Institute Hospital 12 de Octubre","ror":"https://ror.org/002x1sg85","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I4210086614"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Sara Hiller-Vallina","raw_affiliation_strings":["Pathology and Neurooncology Unit, 12 de Octubre University Hospital, Avenida de C\u00f3rdoba sn, Madrid, Community of Madrid, 28041, SPAIN"],"raw_orcid":"https://orcid.org/0000-0001-5973-9991","affiliations":[{"raw_affiliation_string":"Pathology and Neurooncology Unit, 12 de Octubre University Hospital, Avenida de C\u00f3rdoba sn, Madrid, Community of Madrid, 28041, SPAIN","institution_ids":["https://openalex.org/I4210086614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091313837","display_name":"Manuel Villa","orcid":"https://orcid.org/0000-0001-7000-6289"},"institutions":[{"id":"https://openalex.org/I88060688","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02","country_code":"ES","type":"education","lineage":["https://openalex.org/I88060688"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Manuel Villa","raw_affiliation_strings":["Universidad Polit\u00e9cnica de Madrid, Avenida Ramiro de Maeztu 7, Madrid, 28040, SPAIN"],"raw_orcid":"https://orcid.org/0000-0001-7000-6289","affiliations":[{"raw_affiliation_string":"Universidad Polit\u00e9cnica de Madrid, Avenida Ramiro de Maeztu 7, Madrid, 28040, SPAIN","institution_ids":["https://openalex.org/I88060688"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046533689","display_name":"Massimo Salvi","orcid":"https://orcid.org/0000-0001-7225-7401"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Massimo Salvi","raw_affiliation_strings":["Deparment of Electronics and Telecommunications, Politecnico di Torino, Corso Castelfidardo, 30A, Torino, 10129, ITALY"],"raw_orcid":"https://orcid.org/0000-0001-7225-7401","affiliations":[{"raw_affiliation_string":"Deparment of Electronics and Telecommunications, Politecnico di Torino, Corso Castelfidardo, 30A, Torino, 10129, ITALY","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061698320","display_name":"Ricardo Gargini","orcid":"https://orcid.org/0000-0003-4032-0095"},"institutions":[{"id":"https://openalex.org/I4210101691","display_name":"Hospital Universitario 12 De Octubre","ror":"https://ror.org/00qyh5r35","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I4210101691","https://openalex.org/I4210139293"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Ricardo Gargini","raw_affiliation_strings":["Pathology and Neurooncology Unit, Hospital Universitario 12 de Octubre, Avenida de C\u00f3rdoba sn, Madrid, Community of Madrid, 28041, SPAIN"],"raw_orcid":"https://orcid.org/0000-0003-4032-0095","affiliations":[{"raw_affiliation_string":"Pathology and Neurooncology Unit, Hospital Universitario 12 de Octubre, Avenida de C\u00f3rdoba sn, Madrid, Community of Madrid, 28041, SPAIN","institution_ids":["https://openalex.org/I4210101691"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034957952","display_name":"Miguel Chavarr\u00edas","orcid":"https://orcid.org/0000-0003-0280-3440"},"institutions":[{"id":"https://openalex.org/I88060688","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02","country_code":"ES","type":"education","lineage":["https://openalex.org/I88060688"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Miguel Chavarr\u00edas","raw_affiliation_strings":["Universidad Polit\u00e9cnica de Madrid, Avenida Ramiro de Maeztu 7, Madrid, 28040, SPAIN"],"raw_orcid":"https://orcid.org/0000-0003-0280-3440","affiliations":[{"raw_affiliation_string":"Universidad Polit\u00e9cnica de Madrid, Avenida Ramiro de Maeztu 7, Madrid, 28040, SPAIN","institution_ids":["https://openalex.org/I88060688"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5034957952","https://openalex.org/A5046533689","https://openalex.org/A5059637395","https://openalex.org/A5061698320","https://openalex.org/A5091313837","https://openalex.org/A5091922946"],"corresponding_institution_ids":["https://openalex.org/I177477856","https://openalex.org/I4210086614","https://openalex.org/I4210101691","https://openalex.org/I88060688"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":0.6513,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7615629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"6","issue":"4","first_page":"045052","last_page":"045052"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.35120001435279846,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.35120001435279846,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.20479999482631683,"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/T10862","display_name":"AI in cancer detection","score":0.1485999971628189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6462000012397766},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6376000046730042},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6172000169754028},{"id":"https://openalex.org/keywords/morphological-analysis","display_name":"Morphological analysis","score":0.5720999836921692},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5318999886512756},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5303000211715698},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48989999294281006},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.48260000348091125},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.47279998660087585},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.4675000011920929}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7423999905586243},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6462000012397766},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6376000046730042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6326000094413757},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6172000169754028},{"id":"https://openalex.org/C76297474","wikidata":"https://www.wikidata.org/wiki/Q1898737","display_name":"Morphological analysis","level":2,"score":0.5720999836921692},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5318999886512756},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5303000211715698},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.47279998660087585},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.4675000011920929},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4237000048160553},{"id":"https://openalex.org/C2776194525","wikidata":"https://www.wikidata.org/wiki/Q282142","display_name":"Glioblastoma","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3903999924659729},{"id":"https://openalex.org/C188087704","wikidata":"https://www.wikidata.org/wiki/Q369577","display_name":"Standardization","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.33180001378059387},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C3019060180","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automated method","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C125473707","wikidata":"https://www.wikidata.org/wiki/Q9914","display_name":"H&E stain","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C3019831412","wikidata":"https://www.wikidata.org/wiki/Q5778278","display_name":"Fully automatic","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2662000060081482}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1088/2632-2153/ae1f5c","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ae1f5c","pdf_url":null,"source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:dade04714c404117b71cdb24901fc5ec","is_oa":true,"landing_page_url":"https://doaj.org/article/dade04714c404117b71cdb24901fc5ec","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":"Machine Learning: Science and Technology, Vol 6, Iss 4, p 045052 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/ae1f5c","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ae1f5c","pdf_url":null,"source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3614989683","display_name":null,"funder_award_id":"PID2023-148285OB-C44","funder_id":"https://openalex.org/F4320322930","funder_display_name":"Ministerio de Ciencia e Innovaci\u00f3n"},{"id":"https://openalex.org/G6605990891","display_name":null,"funder_award_id":"CP21/00116","funder_id":"https://openalex.org/F4320334923","funder_display_name":"Instituto de Salud Carlos III"},{"id":"https://openalex.org/G6686900828","display_name":null,"funder_award_id":"FI23/00281","funder_id":"https://openalex.org/F4320334923","funder_display_name":"Instituto de Salud Carlos III"},{"id":"https://openalex.org/G8488918144","display_name":null,"funder_award_id":"PI22/01171","funder_id":"https://openalex.org/F4320334923","funder_display_name":"Instituto de Salud Carlos III"}],"funders":[{"id":"https://openalex.org/F4320322930","display_name":"Ministerio de Ciencia e Innovaci\u00f3n","ror":"https://ror.org/034900433"},{"id":"https://openalex.org/F4320334923","display_name":"Instituto de Salud Carlos III","ror":"https://ror.org/00ca2c886"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2006073003","https://openalex.org/W2015255108","https://openalex.org/W2034269086","https://openalex.org/W2096287682","https://openalex.org/W2130201056","https://openalex.org/W2130819084","https://openalex.org/W2132566055","https://openalex.org/W2150461375","https://openalex.org/W2168631000","https://openalex.org/W2782982023","https://openalex.org/W2911964244","https://openalex.org/W2921675148","https://openalex.org/W2952481429","https://openalex.org/W2964309882","https://openalex.org/W2999738634","https://openalex.org/W3006954975","https://openalex.org/W3009926465","https://openalex.org/W3023290436","https://openalex.org/W3036637668","https://openalex.org/W3043656036","https://openalex.org/W3110787789","https://openalex.org/W3130848196","https://openalex.org/W3147353204","https://openalex.org/W3174246647","https://openalex.org/W3191728126","https://openalex.org/W3194280965","https://openalex.org/W4200155097","https://openalex.org/W4200573592","https://openalex.org/W4280557197","https://openalex.org/W4280614820","https://openalex.org/W4282966980","https://openalex.org/W4290096641","https://openalex.org/W4312443924","https://openalex.org/W4366997800","https://openalex.org/W4379260178","https://openalex.org/W4387242297","https://openalex.org/W4387296910","https://openalex.org/W4387782078","https://openalex.org/W4387959434","https://openalex.org/W4389220093","https://openalex.org/W4391541361","https://openalex.org/W4395666209","https://openalex.org/W4396762217","https://openalex.org/W4401414878","https://openalex.org/W4401895759","https://openalex.org/W4402046775","https://openalex.org/W4402220065","https://openalex.org/W4402373459","https://openalex.org/W4402422983","https://openalex.org/W4402581324","https://openalex.org/W4403627568"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Glioblastoma":[1],"(GBM)":[2],"exhibits":[3],"a":[4,54,88,104,111,125,160,166,188,200],"high":[5,112,167],"recurrence":[6],"rate":[7],"of":[8,28,70,93,108,114,127,170,178,190,202],"95%":[9],"due":[10],"to":[11,33,46,66,77],"its":[12,20],"highly":[13],"infiltrative":[14],"nature":[15],"and":[16,31,62,72,95,103,120,138,156,176,196,205],"marked":[17],"heterogeneity,":[18],"making":[19],"diagnosis":[21],"challenging.":[22],"These":[23],"characteristics":[24],"complicate":[25],"the":[26,48,68,79,118,139,174,179],"standardization":[27],"diagnostic":[29,49],"criteria":[30],"contribute":[32],"significant":[34],"interpathologist":[35],"variability.":[36],"In":[37],"response,":[38],"artificial":[39],"intelligence-based":[40],"tools":[41],"are":[42],"increasingly":[43],"being":[44],"developed":[45],"support":[47],"process.":[50],"This":[51],"study":[52],"presents":[53],"complete":[55],"processing":[56],"pipeline":[57],"that":[58],"integrates":[59],"deep":[60],"learning":[61,64],"machine":[63],"techniques":[65],"facilitate":[67],"screening":[69],"hematoxylin":[71],"eosin-stained":[73],"histopathological":[74],"images,":[75],"aiming":[76],"reduce":[78],"workload":[80],"in":[81,165],"pathology":[82],"departments.":[83],"The":[84,145,182],"proposed":[85,180],"workflow":[86],"includes":[87],"convolutional":[89],"neural":[90],"network":[91],"capable":[92],"detecting":[94],"segmenting":[96],"individual":[97],"cells,":[98],"achieving":[99],"89.5%":[100],"pixel-wise":[101],"precision":[102],"DICE":[105],"coefficient":[106],"(DSC)":[107],"88.4%,":[109],"indicating":[110],"degree":[113],"spatial":[115],"overlap":[116],"between":[117,142],"predicted":[119],"reference":[121],"regions.":[122],"Following":[123],"segmentation,":[124],"set":[126],"morphological":[128],"features":[129,147],"is":[130],"extracted,":[131],"including":[132],"cell":[133,136],"area,":[134],"circularity,":[135],"count,":[137],"average":[140],"distance":[141],"neighboring":[143],"cells.":[144,211],"extracted":[146],"were":[148],"subsequently":[149],"used":[150],"for":[151],"tissue":[152],"classification":[153,168],"into":[154],"tumorous":[155],"non-tumorous":[157],"categories":[158],"via":[159],"random":[161],"forest":[162],"algorithm,":[163],"resulting":[164],"accuracy":[169],"94.15%,":[171],"thereby":[172],"suggesting":[173],"robustness":[175],"reliability":[177],"approach.":[181],"applied":[183],"methodology":[184],"was":[185],"validated":[186],"on":[187],"cohort":[189],"82":[191],"patients,":[192],"comprising":[193],"both":[194],"tumor":[195],"healthy":[197],"tissue,":[198],"with":[199],"total":[201],"1557":[203],"images":[204],"more":[206],"than":[207],"4":[208],"million":[209],"annotated":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-11-14T00:00:00"}