{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FIE4POCDTFASRZT3LCAJ5WRYFM","short_pith_number":"pith:FIE4POCD","schema_version":"1.0","canonical_sha256":"2a09c7b843994128e67b58809eda382b28de065d8fa1facae9daa42084fa102d","source":{"kind":"arxiv","id":"2410.23413","version":2},"attestation_state":"computed","paper":{"title":"EchoFM: Foundation Model for Generalizable Echocardiogram Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Chen, Hui Ren, Pengfei Jin, Quanzheng Li, Sekeun Kim, Sifan Song, Tianming Liu, Xiang Li, Yiwei Li","submitted_at":"2024-10-30T19:32:02Z","abstract_excerpt":"Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical foundation models have emerged, solutions for cardiac imaging, especially echocardiography videos, are still unexplored. In this paper, we introduce EchoFM, a foundation model specifically designed to represent and analyze echocardiography videos. In EchoFM, we propose a self-supervised learning framework that captures both spatial and temporal variability patterns through a spatio-temporal consistent masking strategy and"},"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":"2410.23413","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-30T19:32:02Z","cross_cats_sorted":[],"title_canon_sha256":"e10e1a7ecba951966493288cf9d9cab0ecc5bdedc66132869313311470bf5c01","abstract_canon_sha256":"50d13038e3ec49209c9968f6341a28dc644c9b92b96ea6b51b069b287754aa5d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:45.373368Z","signature_b64":"tKgZqtLYq7mzBJyrfBxIUFZoX1eqRRe0RD5wn82Ue82Tm1aJ67FVFY7o91J4RZsSWFZHYmUzXyZKekO0yI/DCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a09c7b843994128e67b58809eda382b28de065d8fa1facae9daa42084fa102d","last_reissued_at":"2026-07-05T10:06:45.372864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:45.372864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EchoFM: Foundation Model for Generalizable Echocardiogram Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Chen, Hui Ren, Pengfei Jin, Quanzheng Li, Sekeun Kim, Sifan Song, Tianming Liu, Xiang Li, Yiwei Li","submitted_at":"2024-10-30T19:32:02Z","abstract_excerpt":"Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical foundation models have emerged, solutions for cardiac imaging, especially echocardiography videos, are still unexplored. In this paper, we introduce EchoFM, a foundation model specifically designed to represent and analyze echocardiography videos. In EchoFM, we propose a self-supervised learning framework that captures both spatial and temporal variability patterns through a spatio-temporal consistent masking strategy and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23413","kind":"arxiv","version":2},"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/2410.23413/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":"2410.23413","created_at":"2026-07-05T10:06:45.372924+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23413v2","created_at":"2026-07-05T10:06:45.372924+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23413","created_at":"2026-07-05T10:06:45.372924+00:00"},{"alias_kind":"pith_short_12","alias_value":"FIE4POCDTFAS","created_at":"2026-07-05T10:06:45.372924+00:00"},{"alias_kind":"pith_short_16","alias_value":"FIE4POCDTFASRZT3","created_at":"2026-07-05T10:06:45.372924+00:00"},{"alias_kind":"pith_short_8","alias_value":"FIE4POCD","created_at":"2026-07-05T10:06:45.372924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.00520","citing_title":"CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15096","citing_title":"Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM","json":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM.json","graph_json":"https://pith.science/api/pith-number/FIE4POCDTFASRZT3LCAJ5WRYFM/graph.json","events_json":"https://pith.science/api/pith-number/FIE4POCDTFASRZT3LCAJ5WRYFM/events.json","paper":"https://pith.science/paper/FIE4POCD"},"agent_actions":{"view_html":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM","download_json":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM.json","view_paper":"https://pith.science/paper/FIE4POCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23413&json=true","fetch_graph":"https://pith.science/api/pith-number/FIE4POCDTFASRZT3LCAJ5WRYFM/graph.json","fetch_events":"https://pith.science/api/pith-number/FIE4POCDTFASRZT3LCAJ5WRYFM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM/action/storage_attestation","attest_author":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM/action/author_attestation","sign_citation":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM/action/citation_signature","submit_replication":"https://pith.science/pith/FIE4POCDTFASRZT3LCAJ5WRYFM/action/replication_record"}},"created_at":"2026-07-05T10:06:45.372924+00:00","updated_at":"2026-07-05T10:06:45.372924+00:00"}