{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IM2UF5OOAILIN4FYMWV3U354LA","short_pith_number":"pith:IM2UF5OO","schema_version":"1.0","canonical_sha256":"433542f5ce021686f0b865abba6fbc5807fad76c1ddaf09c95f38f132bb49dc6","source":{"kind":"arxiv","id":"1811.03433","version":2},"attestation_state":"computed","paper":{"title":"Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Herv\\'e Delingette, Nicholas Ayache, Qiao Zheng","submitted_at":"2018-11-08T14:22:05Z","abstract_excerpt":"We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricu"},"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":"1811.03433","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-08T14:22:05Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"d0dc67ce6601b1ecf0a92d46fd6a909876734f21047d91564ca6a3fc0a274014","abstract_canon_sha256":"0ce281cd28b34e92fc680316756a7abdf480cc1c88acc1aef17ae54e28a067b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:02.423542Z","signature_b64":"UiD6fSf6pH0kbQo5pXLkPZra6sEhvGoehZJKbkurYyTU528OXo1r6zS94RqONbC1vdQ7frhW2sGcYnErvMWHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"433542f5ce021686f0b865abba6fbc5807fad76c1ddaf09c95f38f132bb49dc6","last_reissued_at":"2026-05-17T23:50:02.423068Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:02.423068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Herv\\'e Delingette, Nicholas Ayache, Qiao Zheng","submitted_at":"2018-11-08T14:22:05Z","abstract_excerpt":"We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.03433","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":""},"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":"1811.03433","created_at":"2026-05-17T23:50:02.423129+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.03433v2","created_at":"2026-05-17T23:50:02.423129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.03433","created_at":"2026-05-17T23:50:02.423129+00:00"},{"alias_kind":"pith_short_12","alias_value":"IM2UF5OOAILI","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IM2UF5OOAILIN4FY","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IM2UF5OO","created_at":"2026-05-18T12:32:31.084164+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/IM2UF5OOAILIN4FYMWV3U354LA","json":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA.json","graph_json":"https://pith.science/api/pith-number/IM2UF5OOAILIN4FYMWV3U354LA/graph.json","events_json":"https://pith.science/api/pith-number/IM2UF5OOAILIN4FYMWV3U354LA/events.json","paper":"https://pith.science/paper/IM2UF5OO"},"agent_actions":{"view_html":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA","download_json":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA.json","view_paper":"https://pith.science/paper/IM2UF5OO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.03433&json=true","fetch_graph":"https://pith.science/api/pith-number/IM2UF5OOAILIN4FYMWV3U354LA/graph.json","fetch_events":"https://pith.science/api/pith-number/IM2UF5OOAILIN4FYMWV3U354LA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA/action/storage_attestation","attest_author":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA/action/author_attestation","sign_citation":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA/action/citation_signature","submit_replication":"https://pith.science/pith/IM2UF5OOAILIN4FYMWV3U354LA/action/replication_record"}},"created_at":"2026-05-17T23:50:02.423129+00:00","updated_at":"2026-05-17T23:50:02.423129+00:00"}