{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3BX4KLYVRFQIU6PG72HTV4BC23","short_pith_number":"pith:3BX4KLYV","schema_version":"1.0","canonical_sha256":"d86fc52f1589608a79e6fe8f3af022d6f1d2c60d4d5efae61c3095992d476dd6","source":{"kind":"arxiv","id":"1810.07433","version":1},"attestation_state":"computed","paper":{"title":"Learning to quantify emphysema extent: What labels do we need?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jens Petersen, Laura H. Thomsen, Marleen de Bruijne, Mathilde M. W. Wille, Silas Nyboe {\\O}rting","submitted_at":"2018-10-17T08:48:27Z","abstract_excerpt":"Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variability and standard densitometry approaches to quantify emphysema remain inferior to visual scoring. We explore if machine learning methods that learn from a large dataset of visually assessed CT scans can provide accurate estimates of emphysema extent. We further investigate if machine learning algorithms that learn from a scoring of emphysema extent"},"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":"1810.07433","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-17T08:48:27Z","cross_cats_sorted":[],"title_canon_sha256":"1818f6db84e254ce0e0cde4ae795f20b233cf9726d7235b6b13c9fa68b7e3478","abstract_canon_sha256":"2258e5ddbb0a1dd21ea1232b44ea3776cc999b1c41f056752b5de7cb32cf9102"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:56.410454Z","signature_b64":"XrP0cCl30F512c3YayM5cc5hjZMw2qOXLdjfgiJFBfJSCE9yeykeZQvZQl0qHMMSY872ZYK1vNKeV0eTf1jXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d86fc52f1589608a79e6fe8f3af022d6f1d2c60d4d5efae61c3095992d476dd6","last_reissued_at":"2026-05-18T00:02:56.409843Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:56.409843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to quantify emphysema extent: What labels do we need?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jens Petersen, Laura H. Thomsen, Marleen de Bruijne, Mathilde M. W. Wille, Silas Nyboe {\\O}rting","submitted_at":"2018-10-17T08:48:27Z","abstract_excerpt":"Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variability and standard densitometry approaches to quantify emphysema remain inferior to visual scoring. We explore if machine learning methods that learn from a large dataset of visually assessed CT scans can provide accurate estimates of emphysema extent. We further investigate if machine learning algorithms that learn from a scoring of emphysema extent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07433","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":"1810.07433","created_at":"2026-05-18T00:02:56.409920+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.07433v1","created_at":"2026-05-18T00:02:56.409920+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07433","created_at":"2026-05-18T00:02:56.409920+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BX4KLYVRFQI","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BX4KLYVRFQIU6PG","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BX4KLYV","created_at":"2026-05-18T12:32:02.567920+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/3BX4KLYVRFQIU6PG72HTV4BC23","json":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23.json","graph_json":"https://pith.science/api/pith-number/3BX4KLYVRFQIU6PG72HTV4BC23/graph.json","events_json":"https://pith.science/api/pith-number/3BX4KLYVRFQIU6PG72HTV4BC23/events.json","paper":"https://pith.science/paper/3BX4KLYV"},"agent_actions":{"view_html":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23","download_json":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23.json","view_paper":"https://pith.science/paper/3BX4KLYV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.07433&json=true","fetch_graph":"https://pith.science/api/pith-number/3BX4KLYVRFQIU6PG72HTV4BC23/graph.json","fetch_events":"https://pith.science/api/pith-number/3BX4KLYVRFQIU6PG72HTV4BC23/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23/action/storage_attestation","attest_author":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23/action/author_attestation","sign_citation":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23/action/citation_signature","submit_replication":"https://pith.science/pith/3BX4KLYVRFQIU6PG72HTV4BC23/action/replication_record"}},"created_at":"2026-05-18T00:02:56.409920+00:00","updated_at":"2026-05-18T00:02:56.409920+00:00"}