{"id":"https://openalex.org/W4410296749","doi":"https://doi.org/10.1109/isbi60581.2025.10980735","title":"Attention Aware Continual Learning in Digital Pathology Contexts","display_name":"Attention Aware Continual Learning in Digital Pathology Contexts","publication_year":2025,"publication_date":"2025-04-14","ids":{"openalex":"https://openalex.org/W4410296749","doi":"https://doi.org/10.1109/isbi60581.2025.10980735"},"language":"en","primary_location":{"id":"doi:10.1109/isbi60581.2025.10980735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10980735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074566653","display_name":"Arijit Patra","orcid":"https://orcid.org/0000-0002-4447-6030"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arijit Patra","raw_affiliation_strings":["UCB,Data & Translational Sciences,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UCB,Data & Translational Sciences,UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049571472","display_name":"Tapabrata Chakraborti","orcid":"https://orcid.org/0000-0002-5597-908X"},"institutions":[{"id":"https://openalex.org/I150016208","display_name":"BioMed Central (United Kingdom)","ror":"https://ror.org/0009s9p66","country_code":"GB","type":"company","lineage":["https://openalex.org/I150016208"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tapabrata Chakraborti","raw_affiliation_strings":["UCL,Dept of Med Phys & Biomed Eng,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UCL,Dept of Med Phys & Biomed Eng,UK","institution_ids":["https://openalex.org/I150016208"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06186549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8805000185966492,"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":0.8805000185966492,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.8270000219345093,"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"}},{"id":"https://openalex.org/T13280","display_name":"Biomedical and Engineering Education","score":0.7469000220298767,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.75898277759552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6600931882858276},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.42247113585472107},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.36068230867385864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2841182351112366}],"concepts":[{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.75898277759552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6600931882858276},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.42247113585472107},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.36068230867385864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2841182351112366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi60581.2025.10980735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10980735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W1975767702"],"abstract_inverted_index":{"There":[0],"has":[1,18],"been":[2,19],"a":[3,52,114],"recent":[4],"strategic":[5],"push":[6],"globally":[7],"to":[8,32,68,102,153],"integrate":[9],"digital":[10,138],"pathology":[11,27,139],"analysis":[12,34],"into":[13],"national":[14],"health":[15],"programs,":[16],"which":[17,30,121,151,195],"further":[20],"encouraged":[21],"by":[22,89,165,186],"the":[23,125],"emergence":[24],"of":[25,35,48,66,71,83,124],"large":[26,44],"foundation":[28],"models":[29],"promise":[31],"make":[33],"such":[36,49],"data":[37,72,85,204],"possible":[38],"at":[39],"scale":[40,45],"in":[41,54,76,104,113,137,148,157,176,209],"future.":[42],"However,":[43],"clinical":[46,78],"deployment":[47],"systems":[50],"remain":[51],"challenge":[53],"settings":[55],"where":[56],"datasets":[57],"arrive":[58],"over":[59,73,111],"extended":[60],"time,":[61,112],"and":[62,79,160,174,212],"there":[63],"are":[64,100],"requirements":[65],"adaptation":[67],"new":[69,109,142],"classes":[70,143],"time.":[74],"Also,":[75],"many":[77],"pharmaceutical":[80],"settings,":[81],"retention":[82],"past":[84],"is":[86,122,134],"often":[87,141],"complicated":[88],"privacy":[90],"issues,":[91],"legal":[92],"requirements,":[93],"or":[94],"storage":[95],"constraints.":[96],"Existing":[97],"deep":[98],"architectures":[99],"susceptible":[101],"decline":[103],"performance":[105,201],"when":[106],"trained":[107],"on":[108],"tasks":[110],"phenomenon":[115],"termed":[116],"as":[117,140,163],"catastrophic":[118,184],"forgetting,":[119],"addressing":[120],"one":[123],"key":[126],"challenges":[127],"towards":[128],"enabling":[129],"robust":[130],"learning":[131,190,200],"systems.":[132],"This":[133],"particularly":[135],"compounded":[136],"will":[144],"have":[145],"heterogeneous":[146],"samples":[147],"limited":[149],"numbers":[150],"need":[152],"be":[154],"learned":[155],"robustly":[156],"an":[158,210],"explainable":[159],"trustworthy":[161,214],"manner,":[162],"required":[164],"emerging":[166],"legislation":[167],"worldwide":[168],"that":[169],"call":[170],"for":[171,191],"AI":[172],"safety":[173],"transparency":[175],"high":[177],"risk":[178],"applications":[179],"like":[180],"healthcare.":[181],"We":[182],"mitigate":[183],"forgetting":[185],"designing":[187],"enhanced":[188],"representation":[189],"histology":[192],"image":[193],"analysis,":[194],"not":[196],"only":[197],"improves":[198],"continual":[199],"under":[202],"non-stationary":[203],"environments,":[205],"but":[206],"does":[207],"so":[208],"interpretable,":[211],"hence":[213],"manner.":[215]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}