[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82195-en":3,"doc-seo-82195-105":29,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82195,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Beyond Metadata CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging","Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing. When those metadata disappear, clinically critical failure modes can be hidden by strong aggregate performance, and robust-learning methods lose the group structure they depend on. CAPRA introduces a calibrated proxy-axis framework for hidden subgroup analysis under missing metadata by predicting semantic axes from images, calibrating axis posteriors via patient-level cross-fitting, and exposing a reusable calibrated subgroup interface. Across multiple imaging modalities, it uncovers disparity patterns missed by metadata slicing, remains informative under dataset shift, and aligns subgroup partitions with interpretable failure axes while improving downstream robust learning without requiring subgroup labels at deployment.","Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging  \n*  \nYawen Li, Yan Li, Zhe Xue, Yingxia Shao, Meiyu Liang, Guanhua Ye  \nBeijing University of Posts and Telecommunications  \nBeijing, China  \n{warmly0716,liyanly, xuezhe, shaoyx,meiyu1210, [g.ye}@bupt.edu.cn](g.ye}@bupt.edu.cn)  \narXiv :2607 .09102v1 [ ee ss .IV] 10 Jul 2026  \nAbstract  \nMedical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing. Once those metadata disappear, clinically critical failure modes can be masked by strong aggregate performance, and many robust-learning methods lose the group structure they rely on. We present CAPRA, a calibrated proxy-axis framework for hidden subgroup analysis under missing metadata. CAPRA predicts image-derived semantic axes, calibrates axis posteriors on a small metadata-labeled split via patient-level cross-fitting, and organizes those posteriors into a calibrated subgroup interface that supports both deployment-time failure analysis and downstream robust learning without requiring subgroup labels at deployment. Across fundus, dermoscopy, and chest radiography, CAPRA reveals disparity patterns missed by metadata-only slicing, remains informative under dataset shift, and produces subgroup partitions that align more closely with explicit failure axes than image-only or latent-slice baselines. The same interface can also be reused by downstream robust learners, although those gains are domaindependent. Overall, CAPRA turns hidden subgroup analysis under missing metadata into a calibrated, interpretable, and reusable subgroup interface for deployment-time analysis and robust transfer.  \nCCS Concepts  \n• Applied computing → Health informatics; • Computing methodologies → Computer vision problems; Neural networks; Artificial intelligence.  \nKeywords  \nMedical Imaging, Multimodal Learning, Subgroup Discovery, Robust Learning  \nACM Reference Format:  \n*  \nYawen Li, Yan Li, Zhe Xue, Yingxia Shao, Meiyu Liang, Guanhua Ye . 2026. Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging. In . ACM, New York, NY, USA, 10 pages. [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n*Corresponding author.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nConference’17, Washington, DC, USA  \n© 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-x-xxxx-xxxx-x/YYYY/MM [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 Introduction  \nMedical imaging models are usually developed on curated research cohorts with relatively complete demographic, device, and acquisition metadata. Clinical deployment looks different. Patient demographics may be missing from imaging workflows, device provenance may be inconsistently recorded, and formal quality annotations may not exist at all. Public datasets often reflect the same documentation gap: metadata are incomplete, uneven across attributes, or simply not rich enough for deployment-facing subgroup audit [8, 14, 22, 23, 35] . The consequence is familiar but still underaddressed. A model can look reliable in aggregate while failing on clinically meaningful subsets defined by technical, anatomical, or phenotypic variation, and those failures may remain invisible prec","cbCaidkX9igfbryD","https://ap.wps.com/l/cbCaidkX9igfbryD","pdf",4477474,1,10,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What benefits does CAPRA provide compared with metadata-only or latent-slice baselines?\",\"answer\":\"CAPRA reveals disparity patterns missed by metadata-only slicing, stays informative under dataset shift, and produces subgroup partitions that better align with explicit failure axes than image-only or latent-slice approaches.\"}]",1784178750,25,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":27},"beyond-metadata-capra-for-hidden-subgroup-analysis-under-missing-metadata-in-medical-imaging","",{"@graph":35,"@context":77},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/beyond-metadata-capra-for-hidden-subgroup-analysis-under-missing-metadata-in-medical-imaging/82195/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What benefits does CAPRA provide compared with metadata-only or latent-slice baselines?","Question",{"text":75,"@type":76},"CAPRA reveals disparity patterns missed by metadata-only slicing, stays informative under dataset shift, and produces subgroup partitions that better align with explicit failure axes than image-only or latent-slice approaches.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":45,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":45,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":45,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]