{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NZIOW4YBF5KC6GIPMWAHR3MUMM","short_pith_number":"pith:NZIOW4YB","schema_version":"1.0","canonical_sha256":"6e50eb73012f542f190f658078ed94632cff0eaea8c359cf978300c7c664fe67","source":{"kind":"arxiv","id":"2605.17729","version":1},"attestation_state":"computed","paper":{"title":"Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Danu Kim","submitted_at":"2026-05-18T01:16:48Z","abstract_excerpt":"Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains limited due to variations in imaging devices, acquisition protocols, and institutional conditions. This study introduces a replay-based domain-incremental continual learning designed to enable continual adaptation to cross-domain variations without catastrophic forgetting. The proposed method incorporates a class-aware balanced replay to maintain balanced class representation within a constrained memory and a class-aware loss to dynamically reweigh"},"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":"2605.17729","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T01:16:48Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3ac0bb585e21c4e03de3dc152a35530d439d61bfc9d74ca895d5cfdae0b795ff","abstract_canon_sha256":"84b8946b4e0d3e9dc0b3cd6e204a40666129675aab4c27e7528549ae72d28421"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:55.135706Z","signature_b64":"XHn7W6RnnSrOYVbRE00WdPX5EdWY9dn6OHYEThHFVejnu0eaZ7ubX9AKE8OHB36Z06UH0a5guTpPU7RQJIMnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e50eb73012f542f190f658078ed94632cff0eaea8c359cf978300c7c664fe67","last_reissued_at":"2026-05-20T00:04:55.134848Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:55.134848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Danu Kim","submitted_at":"2026-05-18T01:16:48Z","abstract_excerpt":"Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains limited due to variations in imaging devices, acquisition protocols, and institutional conditions. This study introduces a replay-based domain-incremental continual learning designed to enable continual adaptation to cross-domain variations without catastrophic forgetting. The proposed method incorporates a class-aware balanced replay to maintain balanced class representation within a constrained memory and a class-aware loss to dynamically reweigh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17729","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/2605.17729/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":"2605.17729","created_at":"2026-05-20T00:04:55.134995+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17729v1","created_at":"2026-05-20T00:04:55.134995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17729","created_at":"2026-05-20T00:04:55.134995+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZIOW4YBF5KC","created_at":"2026-05-20T00:04:55.134995+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZIOW4YBF5KC6GIP","created_at":"2026-05-20T00:04:55.134995+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZIOW4YB","created_at":"2026-05-20T00:04:55.134995+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/NZIOW4YBF5KC6GIPMWAHR3MUMM","json":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM.json","graph_json":"https://pith.science/api/pith-number/NZIOW4YBF5KC6GIPMWAHR3MUMM/graph.json","events_json":"https://pith.science/api/pith-number/NZIOW4YBF5KC6GIPMWAHR3MUMM/events.json","paper":"https://pith.science/paper/NZIOW4YB"},"agent_actions":{"view_html":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM","download_json":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM.json","view_paper":"https://pith.science/paper/NZIOW4YB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17729&json=true","fetch_graph":"https://pith.science/api/pith-number/NZIOW4YBF5KC6GIPMWAHR3MUMM/graph.json","fetch_events":"https://pith.science/api/pith-number/NZIOW4YBF5KC6GIPMWAHR3MUMM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM/action/storage_attestation","attest_author":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM/action/author_attestation","sign_citation":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM/action/citation_signature","submit_replication":"https://pith.science/pith/NZIOW4YBF5KC6GIPMWAHR3MUMM/action/replication_record"}},"created_at":"2026-05-20T00:04:55.134995+00:00","updated_at":"2026-05-20T00:04:55.134995+00:00"}