{"id":"https://openalex.org/W4405304837","doi":"https://doi.org/10.1109/sipaim62974.2024.10783583","title":"Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions","display_name":"Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions","publication_year":2024,"publication_date":"2024-11-13","ids":{"openalex":"https://openalex.org/W4405304837","doi":"https://doi.org/10.1109/sipaim62974.2024.10783583"},"language":"en","primary_location":{"id":"doi:10.1109/sipaim62974.2024.10783583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim62974.2024.10783583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)","raw_type":"proceedings-article"},"type":"article","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/A5115407819","display_name":"Santiago Aguilera-Gonz\u00e1lez","orcid":null},"institutions":[{"id":"https://openalex.org/I41047195","display_name":"Military University Nueva Granada","ror":"https://ror.org/05n0gsn30","country_code":"CO","type":"education","lineage":["https://openalex.org/I41047195"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Santiago Aguilera-Gonz\u00e1lez","raw_affiliation_strings":["Universidad Militar Nueva Granada,Facultad de Ingeniería,Bogotá,D.C,Colombia,110111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Militar Nueva Granada,Facultad de Ingeniería,Bogotá,D.C,Colombia,110111","institution_ids":["https://openalex.org/I41047195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090860642","display_name":"Diego Renza","orcid":"https://orcid.org/0000-0001-8073-3594"},"institutions":[{"id":"https://openalex.org/I41047195","display_name":"Military University Nueva Granada","ror":"https://ror.org/05n0gsn30","country_code":"CO","type":"education","lineage":["https://openalex.org/I41047195"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Diego Renza","raw_affiliation_strings":["Universidad Militar Nueva Granada,Facultad de Ingeniería,Bogotá,D.C,Colombia,110111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Militar Nueva Granada,Facultad de Ingeniería,Bogotá,D.C,Colombia,110111","institution_ids":["https://openalex.org/I41047195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059215579","display_name":"Ernesto Moya-Albor","orcid":"https://orcid.org/0000-0002-9637-786X"},"institutions":[{"id":"https://openalex.org/I4210160924","display_name":"Universidad Panamericana","ror":"https://ror.org/05pd6sh18","country_code":"GT","type":"education","lineage":["https://openalex.org/I4210160924"]}],"countries":["GT"],"is_corresponding":false,"raw_author_name":"Ernesto Moya-Albor","raw_affiliation_strings":["Universidad Panamericana,Facultad de Ingeniería,Ciudad de México,México,03920"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Panamericana,Facultad de Ingeniería,Ciudad de México,México,03920","institution_ids":["https://openalex.org/I4210160924"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6109,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75608629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.916700005531311,"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":0.916700005531311,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9128000140190125,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9122999906539917,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6733376979827881},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6422144770622253},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6371361613273621},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6148082613945007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5708388686180115},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4744492769241333}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6733376979827881},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6422144770622253},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6371361613273621},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6148082613945007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5708388686180115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4744492769241333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sipaim62974.2024.10783583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim62974.2024.10783583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2945487040","https://openalex.org/W3195923711","https://openalex.org/W4317436377","https://openalex.org/W4366494274","https://openalex.org/W4379421972","https://openalex.org/W4388792466","https://openalex.org/W4394894234","https://openalex.org/W6796673453","https://openalex.org/W6801968024","https://openalex.org/W6838333041","https://openalex.org/W6846964422"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"One":[0],"of":[1,52,70,83,93,147,156],"the":[2,13,26,31,50,58,68,81,91,98,154,157],"conditions":[3],"expected":[4],"when":[5],"training":[6,27,65],"a":[7,53,66,77,150],"machine":[8,54],"learning":[9,55],"model":[10],"is":[11,41,60,103,113,141],"that":[12,125],"inference":[14],"data":[15],"should":[16],"be":[17,38],"independently":[18],"and":[19,111,134],"identically":[20],"distributed":[21],"(i.i.d.)":[22],"with":[23],"respect":[24],"to":[25,62,79,97,115,137,143,162],"data.":[28],"However,":[29],"as":[30,43,159,161],"real":[32],"world":[33],"evolves,":[34],"this":[35,74,138],"condition":[36],"can":[37,48],"lost,":[39],"which":[40,145],"known":[42],"shift":[44,71,85],"distribution.":[45,72],"This":[46],"situation":[47],"affect":[49],"performance":[51],"model,":[56],"so":[57],"question":[59],"how":[61],"evaluate":[63,144],"(without":[64],"model)":[67],"presence":[69],"Consequently,":[73],"paper":[75],"presents":[76],"proposal":[78],"determine":[80,163],"degree":[82],"distribution":[84],"in":[86,90],"medical":[87],"image":[88],"datasets":[89,119],"face":[92],"possible":[94,142],"distortions":[95],"due":[96],"capture":[99],"system.":[100],"The":[101],"methodology":[102],"based":[104],"on":[105,153],"Cumulative":[106],"Spectral":[107],"Gradient":[108],"(CSG)":[109],"metric":[110],"it":[112,140],"applied":[114],"three":[116],"biomedical":[117,130],"imaging":[118],"extracted":[120],"from":[121],"MedMNIST,":[122],"an":[123],"initiative":[124],"has":[126],"compiled":[127],"several":[128],"standardized":[129],"datasets:":[131],"PneumoniaMNIST,":[132],"BreastMNIST":[133],"RetinaMNIST.":[135],"Thanks":[136],"methodology,":[139],"types":[146],"modifications":[148],"have":[149],"greater":[151],"impact":[152],"generalization":[155],"models,":[158],"well":[160],"if":[164],"there":[165],"are":[166],"classes":[167],"more":[168],"affected":[169],"by":[170],"corruptions.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}