{"id":"https://openalex.org/W4414818360","doi":"https://doi.org/10.1109/icassp55912.2026.11463914","title":"Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring","display_name":"Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W4414818360","doi":"https://doi.org/10.1109/icassp55912.2026.11463914"},"language":"en","primary_location":{"id":"doi:10.1109/icassp55912.2026.11463914","doi":"https://doi.org/10.1109/icassp55912.2026.11463914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp55912.2026.11463914","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.01683","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100582040","display_name":"H. Shu","orcid":"https://orcid.org/0009-0007-9134-1888","observed_orcids":["https://orcid.org/0009-0007-9134-1888"]},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Han-Jay Shu","raw_affiliation_strings":["National Tsing Hua University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Computer Science","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101286955","display_name":"Wei-Ning Chiu","orcid":"https://orcid.org/0009-0005-2492-7403","observed_orcids":["https://orcid.org/0009-0005-2492-7403"]},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wei-Ning Chiu","raw_affiliation_strings":["National Taiwan University,Department of Computer Science and Information Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University,Department of Computer Science and Information Engineering","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108925542","display_name":"Shun-Ting Chang","orcid":"https://orcid.org/0009-0007-5830-8306","observed_orcids":["https://orcid.org/0009-0007-5830-8306"]},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shun-Ting Chang","raw_affiliation_strings":["National Tsing Hua University,Department of Electrical Engineering and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Electrical Engineering and Computer Science","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Meng-Ping Huang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Meng-Ping Huang","raw_affiliation_strings":["National Tsing Hua University,Department of Technology Management"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Technology Management","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026341995","display_name":"Takeshi Tohyama","orcid":"https://orcid.org/0000-0002-7300-8180","observed_orcids":["https://orcid.org/0000-0002-7300-8180"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Takeshi Tohyama","raw_affiliation_strings":["Massachusetts Institute of Technology,Laboratory for Computational Physiology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Laboratory for Computational Physiology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112676676","display_name":"Ahram Han","orcid":"https://orcid.org/0000-0002-3866-5214","observed_orcids":["https://orcid.org/0000-0002-3866-5214"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahram Han","raw_affiliation_strings":["Massachusetts Institute of Technology,Laboratory for Computational Physiology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Laboratory for Computational Physiology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103017921","display_name":"Po\u2010Chih Kuo","orcid":"https://orcid.org/0000-0003-4020-3147","observed_orcids":["https://orcid.org/0000-0003-4020-3147"]},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Po-Chih Kuo","raw_affiliation_strings":["National Tsing Hua University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Computer Science","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.5659,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.9395331,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"6736","last_page":"6740"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.4733999967575073,"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"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.4733999967575073,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.37940001487731934,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.10670000314712524,"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/chest-x-ray-interpretation","display_name":"chest X-ray interpretation","score":0.8230000138282776},{"id":"https://openalex.org/keywords/overconfidence","display_name":"overconfidence","score":0.6970000267028809},{"id":"https://openalex.org/keywords/risk-prediction-score","display_name":"risk scoring","score":0.5529999732971191},{"id":"https://openalex.org/keywords/image-rotation","display_name":"image rotation","score":0.42800000309944153},{"id":"https://openalex.org/keywords/confidence-calibration","display_name":"confidence calibration","score":0.7379999756813049},{"id":"https://openalex.org/keywords/selective-prediction","display_name":"selective prediction","score":0.32100000977516174}],"concepts":[{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.597100019454956},{"id":"https://openalex.org/C51110983","wikidata":"https://www.wikidata.org/wiki/Q16503490","display_name":"Overconfidence effect","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.49570000171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4611999988555908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42649999260902405},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4049000144004822},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C2781137159","wikidata":"https://www.wikidata.org/wiki/Q1283318","display_name":"Chest radiograph","level":3,"score":0.3700000047683716},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35280001163482666},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3452000021934509},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.335099995136261},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3203999996185303},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C2778414658","wikidata":"https://www.wikidata.org/wiki/Q1409206","display_name":"Model risk","level":3,"score":0.3052999973297119},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.289900004863739},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.289000004529953},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C2779328685","wikidata":"https://www.wikidata.org/wiki/Q1475557","display_name":"Patient safety","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp55912.2026.11463914","doi":"https://doi.org/10.1109/icassp55912.2026.11463914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp55912.2026.11463914","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.01683","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.01683","pdf_url":"https://arxiv.org/pdf/2510.01683","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.01683","doi":"https://doi.org/10.48550/arxiv.2510.01683","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.01683","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.01683","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.01683","pdf_url":"https://arxiv.org/pdf/2510.01683","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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/W4414818360.pdf","grobid_xml":"https://content.openalex.org/works/W4414818360.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2752747624","https://openalex.org/W2767414122","https://openalex.org/W2995225687","https://openalex.org/W3183048323","https://openalex.org/W4200191097","https://openalex.org/W4291730167","https://openalex.org/W4304195579","https://openalex.org/W4319300958","https://openalex.org/W4387821331","https://openalex.org/W4388834385","https://openalex.org/W4394659038","https://openalex.org/W4396820464","https://openalex.org/W4399439301","https://openalex.org/W4406353025","https://openalex.org/W4416074427"],"related_works":["https://openalex.org/W7201984620","https://openalex.org/W3138913153","https://openalex.org/W7134092140","https://openalex.org/W4392672760","https://openalex.org/W7161627839","https://openalex.org/W7169761817","https://openalex.org/W4391304806","https://openalex.org/W4391422211","https://openalex.org/W7219660580","https://openalex.org/W7204134544"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2],"achieve":[3],"strong":[4],"performance":[5],"in":[6,31,57,125],"chest":[7],"radiograph":[8],"(CXR)":[9],"interpretation,":[10],"yet":[11],"fairness":[12,122],"and":[13,51,79,108,118,123],"reliability":[14],"concerns":[15],"persist.":[16],"Models":[17],"often":[18],"show":[19,98],"uneven":[20],"accuracy":[21],"across":[22],"patient":[23],"subgroups,":[24],"leading":[25],"to":[26,68,103],"hidden":[27],"failures":[28],"not":[29],"reflected":[30],"aggregate":[32],"metrics.":[33],"Existing":[34],"error":[35],"detection":[36],"approaches\u2014based":[37],"on":[38],"confidence":[39],"calibration":[40],"or":[41],"out-of-distribution":[42],"(OOD)":[43],"detection\u2014struggle":[44],"with":[45,83],"subtle":[46],"within-distribution":[47],"errors,":[48],"while":[49],"image-":[50],"representation-level":[52],"consistency-based":[53],"methods":[54],"remain":[55],"underexplored":[56],"medical":[58,126],"imaging.":[59],"We":[60],"propose":[61],"an":[62],"augmentation-sensitivity":[63],"risk":[64],"scoring":[65],"(ASRS)":[66],"framework":[67],"identify":[69],"error-prone":[70],"CXR":[71],"cases.":[72],"ASRS":[73,110],"applies":[74],"clinically":[75],"plausible":[76],"rotations":[77],"(\u00b115\u00b0/\u00b130\u00b0)":[78],"measures":[80],"embedding":[81],"shifts":[82],"the":[84],"RAD-DINO":[85],"encoder.":[86],"Sensitivity":[87],"scores":[88],"stratify":[89],"samples":[90],"into":[91],"stability":[92],"quartiles,":[93],"where":[94],"highly":[95],"sensitive":[96],"cases":[97],"substantially":[99],"lower":[100],"recall":[101],"(\u22120.2":[102],"\u22120.3)":[104],"despite":[105],"high":[106],"AUROC":[107],"confidence.":[109],"provides":[111],"a":[112],"label-free":[113],"means":[114],"for":[115],"selective":[116],"prediction":[117],"clinician":[119],"review,":[120],"improving":[121],"safety":[124],"AI.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
