{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XXS5WQL246BUKOEGUS3XBQBVD2","short_pith_number":"pith:XXS5WQL2","schema_version":"1.0","canonical_sha256":"bde5db417ae783453886a4b770c0351ea4167fdad40a8bdce00e3c5942617283","source":{"kind":"arxiv","id":"2506.20683","version":1},"attestation_state":"computed","paper":{"title":"Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","eess.SP"],"primary_cat":"eess.IV","authors_text":"Alexander Selivanov, Daniel R\\\"uckert, Nil Stolt-Ans\\'o, \\\"Ozg\\\"un Turgut, Philip M\\\"uller","submitted_at":"2025-06-24T17:19:39Z","abstract_excerpt":"An electrocardiogram (ECG) is a widely used, cost-effective tool for detecting electrical abnormalities in the heart. However, it cannot directly measure functional parameters, such as ventricular volumes and ejection fraction, which are crucial for assessing cardiac function. Cardiac magnetic resonance (CMR) is the gold standard for these measurements, providing detailed structural and functional insights, but is expensive and less accessible. To bridge this gap, we propose PTACL (Patient and Temporal Alignment Contrastive Learning), a multimodal contrastive learning framework that enhances E"},"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":"2506.20683","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-24T17:19:39Z","cross_cats_sorted":["cs.AI","cs.CV","eess.SP"],"title_canon_sha256":"d095bf143347e9533de879100acec6a776212cb2a9f2f685a5a2d3425286c0e0","abstract_canon_sha256":"81f448212b92df9084402b5f44c9dbb11070fef3d2a86b271488da0d9791ae3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:32.336379Z","signature_b64":"t2cYtzzSCOtFiROWzOdTLhVf26Ylajd8RVSLzxWapqzJriY1JqZrrc28FZCDM4aNLayfcxZW/eCchwHOydGOCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bde5db417ae783453886a4b770c0351ea4167fdad40a8bdce00e3c5942617283","last_reissued_at":"2026-07-05T11:27:32.335812Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:32.335812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","eess.SP"],"primary_cat":"eess.IV","authors_text":"Alexander Selivanov, Daniel R\\\"uckert, Nil Stolt-Ans\\'o, \\\"Ozg\\\"un Turgut, Philip M\\\"uller","submitted_at":"2025-06-24T17:19:39Z","abstract_excerpt":"An electrocardiogram (ECG) is a widely used, cost-effective tool for detecting electrical abnormalities in the heart. However, it cannot directly measure functional parameters, such as ventricular volumes and ejection fraction, which are crucial for assessing cardiac function. Cardiac magnetic resonance (CMR) is the gold standard for these measurements, providing detailed structural and functional insights, but is expensive and less accessible. To bridge this gap, we propose PTACL (Patient and Temporal Alignment Contrastive Learning), a multimodal contrastive learning framework that enhances E"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20683","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/2506.20683/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":"2506.20683","created_at":"2026-07-05T11:27:32.335878+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.20683v1","created_at":"2026-07-05T11:27:32.335878+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20683","created_at":"2026-07-05T11:27:32.335878+00:00"},{"alias_kind":"pith_short_12","alias_value":"XXS5WQL246BU","created_at":"2026-07-05T11:27:32.335878+00:00"},{"alias_kind":"pith_short_16","alias_value":"XXS5WQL246BUKOEG","created_at":"2026-07-05T11:27:32.335878+00:00"},{"alias_kind":"pith_short_8","alias_value":"XXS5WQL2","created_at":"2026-07-05T11:27:32.335878+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.02605","citing_title":"Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2","json":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2.json","graph_json":"https://pith.science/api/pith-number/XXS5WQL246BUKOEGUS3XBQBVD2/graph.json","events_json":"https://pith.science/api/pith-number/XXS5WQL246BUKOEGUS3XBQBVD2/events.json","paper":"https://pith.science/paper/XXS5WQL2"},"agent_actions":{"view_html":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2","download_json":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2.json","view_paper":"https://pith.science/paper/XXS5WQL2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.20683&json=true","fetch_graph":"https://pith.science/api/pith-number/XXS5WQL246BUKOEGUS3XBQBVD2/graph.json","fetch_events":"https://pith.science/api/pith-number/XXS5WQL246BUKOEGUS3XBQBVD2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2/action/storage_attestation","attest_author":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2/action/author_attestation","sign_citation":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2/action/citation_signature","submit_replication":"https://pith.science/pith/XXS5WQL246BUKOEGUS3XBQBVD2/action/replication_record"}},"created_at":"2026-07-05T11:27:32.335878+00:00","updated_at":"2026-07-05T11:27:32.335878+00:00"}