{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DFTO3JXBKD24K7TZCBMCPJ3M5Z","short_pith_number":"pith:DFTO3JXB","schema_version":"1.0","canonical_sha256":"1966eda6e150f5c57e79105827a76cee735b4d53e0a57b3c8cfe63b4f17fdc23","source":{"kind":"arxiv","id":"1812.06061","version":1},"attestation_state":"computed","paper":{"title":"Automatic quantification of the LV function and mass: a deep learning approach for cardiovascular MRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ariel H. Curiale, Flavio D. Colavecchia, German Mato","submitted_at":"2018-12-14T18:20:39Z","abstract_excerpt":"Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).\n  Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance"},"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":"1812.06061","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-14T18:20:39Z","cross_cats_sorted":[],"title_canon_sha256":"64350a38337bc2fd5153c57bb083974ae59a04e1a9e92177d126d216bb15594f","abstract_canon_sha256":"9d3f741bc84aee368683b1e6de81d00779a7e5a826a0e5325eae7bf189b32091"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:15.676672Z","signature_b64":"CaQzbrdTFJ4fmg20q4LrLaSaTareRXwt9/lKbGhFZhtz/r1/NKD65XnO0aJ28nb7g8V66hF6uD4H15AwMTSsBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1966eda6e150f5c57e79105827a76cee735b4d53e0a57b3c8cfe63b4f17fdc23","last_reissued_at":"2026-05-17T23:58:15.676135Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:15.676135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic quantification of the LV function and mass: a deep learning approach for cardiovascular MRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ariel H. Curiale, Flavio D. Colavecchia, German Mato","submitted_at":"2018-12-14T18:20:39Z","abstract_excerpt":"Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).\n  Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06061","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":""},"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":"1812.06061","created_at":"2026-05-17T23:58:15.676212+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.06061v1","created_at":"2026-05-17T23:58:15.676212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06061","created_at":"2026-05-17T23:58:15.676212+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFTO3JXBKD24","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFTO3JXBKD24K7TZ","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFTO3JXB","created_at":"2026-05-18T12:32:19.392346+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/DFTO3JXBKD24K7TZCBMCPJ3M5Z","json":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z.json","graph_json":"https://pith.science/api/pith-number/DFTO3JXBKD24K7TZCBMCPJ3M5Z/graph.json","events_json":"https://pith.science/api/pith-number/DFTO3JXBKD24K7TZCBMCPJ3M5Z/events.json","paper":"https://pith.science/paper/DFTO3JXB"},"agent_actions":{"view_html":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z","download_json":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z.json","view_paper":"https://pith.science/paper/DFTO3JXB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.06061&json=true","fetch_graph":"https://pith.science/api/pith-number/DFTO3JXBKD24K7TZCBMCPJ3M5Z/graph.json","fetch_events":"https://pith.science/api/pith-number/DFTO3JXBKD24K7TZCBMCPJ3M5Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z/action/storage_attestation","attest_author":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z/action/author_attestation","sign_citation":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z/action/citation_signature","submit_replication":"https://pith.science/pith/DFTO3JXBKD24K7TZCBMCPJ3M5Z/action/replication_record"}},"created_at":"2026-05-17T23:58:15.676212+00:00","updated_at":"2026-05-17T23:58:15.676212+00:00"}