{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PFJOCAW4F4SJZFUFV3G7JAI7FX","short_pith_number":"pith:PFJOCAW4","schema_version":"1.0","canonical_sha256":"7952e102dc2f249c9685aecdf4811f2de63586d196c04d74b31becd8964aba60","source":{"kind":"arxiv","id":"2201.03559","version":1},"attestation_state":"computed","paper":{"title":"Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Marina M.-C. H\\\"ohne, Michael Kampffmeyer, Robert Jenssen, Srishti Gautam, Stine Hansen","submitted_at":"2022-01-10T14:57:39Z","abstract_excerpt":"The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to improve performance by recognizing the source domain of an image rather than the medical pathology. We hypothesize that this effect is enforced by and leverages label-imbalance across the source domains, i.e, prevalence of a disease corresponding to a source. Therefore, in this work, we perform a thorough study of the effect of label-imbalance in multi-source training for the task of pneumonia detection on the widely use"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2201.03559","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-10T14:57:39Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"90723ee848a8124143ec6aa2bfc740db305775da3e610363ab6b9880f036ee85","abstract_canon_sha256":"2bf5813b10cc2244bb9ab2704963ff1335eee84944343907f59cb940b73c9de9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:47:31.003490Z","signature_b64":"tLNmkGxiAPl33GgIpQ5fUaPO8fDMD+gaNZU5ln3zk7hBplUlo93cmcw5n7w+igOGMkiM24Q58/1pPNoZm1eFBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7952e102dc2f249c9685aecdf4811f2de63586d196c04d74b31becd8964aba60","last_reissued_at":"2026-07-05T03:47:31.003075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:47:31.003075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Marina M.-C. H\\\"ohne, Michael Kampffmeyer, Robert Jenssen, Srishti Gautam, Stine Hansen","submitted_at":"2022-01-10T14:57:39Z","abstract_excerpt":"The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to improve performance by recognizing the source domain of an image rather than the medical pathology. We hypothesize that this effect is enforced by and leverages label-imbalance across the source domains, i.e, prevalence of a disease corresponding to a source. Therefore, in this work, we perform a thorough study of the effect of label-imbalance in multi-source training for the task of pneumonia detection on the widely use"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.03559","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2201.03559/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2201.03559","created_at":"2026-07-05T03:47:31.003129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.03559v1","created_at":"2026-07-05T03:47:31.003129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.03559","created_at":"2026-07-05T03:47:31.003129+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFJOCAW4F4SJ","created_at":"2026-07-05T03:47:31.003129+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFJOCAW4F4SJZFUF","created_at":"2026-07-05T03:47:31.003129+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFJOCAW4","created_at":"2026-07-05T03:47:31.003129+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX","json":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX.json","graph_json":"https://pith.science/api/pith-number/PFJOCAW4F4SJZFUFV3G7JAI7FX/graph.json","events_json":"https://pith.science/api/pith-number/PFJOCAW4F4SJZFUFV3G7JAI7FX/events.json","paper":"https://pith.science/paper/PFJOCAW4"},"agent_actions":{"view_html":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX","download_json":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX.json","view_paper":"https://pith.science/paper/PFJOCAW4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.03559&json=true","fetch_graph":"https://pith.science/api/pith-number/PFJOCAW4F4SJZFUFV3G7JAI7FX/graph.json","fetch_events":"https://pith.science/api/pith-number/PFJOCAW4F4SJZFUFV3G7JAI7FX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX/action/storage_attestation","attest_author":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX/action/author_attestation","sign_citation":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX/action/citation_signature","submit_replication":"https://pith.science/pith/PFJOCAW4F4SJZFUFV3G7JAI7FX/action/replication_record"}},"created_at":"2026-07-05T03:47:31.003129+00:00","updated_at":"2026-07-05T03:47:31.003129+00:00"}