{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L6TPRTSRLKMADQDXYMW4QYI7WW","short_pith_number":"pith:L6TPRTSR","schema_version":"1.0","canonical_sha256":"5fa6f8ce515a9801c077c32dc8611fb591aaea8315e23e5e42c4e892fa763133","source":{"kind":"arxiv","id":"2502.18225","version":3},"attestation_state":"computed","paper":{"title":"Liver Cirrhosis Stage Estimation from MRI with Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alpay Medetalibeyoglu, Amir A. Borhani, Daniela P. Ladner, Debesh Jha, Elif Keles, Ertugrul Aktas, Federica Proietto Salanitri, Gorkem Durak, Jun Zeng, Matthew Antalek, Ulas Bagci","submitted_at":"2025-02-23T20:50:08Z","abstract_excerpt":"We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI. Cirrhosis is the severe scarring (fibrosis) of the liver and a common endpoint of various chronic liver diseases. Early diagnosis is vital to prevent complications such as decompensation and cancer, which significantly decreases life expectancy. However, diagnosing cirrhosis in its early stages is challenging, and patients often present with life-threatening complications. Our approach integrates multi-scale feature learning with sequence-specific attention mechanisms to cap"},"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":"2502.18225","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-02-23T20:50:08Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"9a62bcfcb129cc2dea1fe38255a8bb603649952b9f96be1de098d9dcd117814f","abstract_canon_sha256":"bae349050b5a3e36f0eaf62c5e43c9ce331e50cbe9fdb9136c684aba561b4688"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:20.993652Z","signature_b64":"u1zI9JpacI/zPYwvlm9vuIP4pYF/jXwJsIIpjZrkVQI8VfH9gjBAckFpvQN5jJqZmJixS0SBxK95WtLKSnufDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fa6f8ce515a9801c077c32dc8611fb591aaea8315e23e5e42c4e892fa763133","last_reissued_at":"2026-07-05T11:07:20.993117Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:20.993117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Liver Cirrhosis Stage Estimation from MRI with Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alpay Medetalibeyoglu, Amir A. Borhani, Daniela P. Ladner, Debesh Jha, Elif Keles, Ertugrul Aktas, Federica Proietto Salanitri, Gorkem Durak, Jun Zeng, Matthew Antalek, Ulas Bagci","submitted_at":"2025-02-23T20:50:08Z","abstract_excerpt":"We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI. Cirrhosis is the severe scarring (fibrosis) of the liver and a common endpoint of various chronic liver diseases. Early diagnosis is vital to prevent complications such as decompensation and cancer, which significantly decreases life expectancy. However, diagnosing cirrhosis in its early stages is challenging, and patients often present with life-threatening complications. Our approach integrates multi-scale feature learning with sequence-specific attention mechanisms to cap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.18225","kind":"arxiv","version":3},"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/2502.18225/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":"2502.18225","created_at":"2026-07-05T11:07:20.993186+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.18225v3","created_at":"2026-07-05T11:07:20.993186+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.18225","created_at":"2026-07-05T11:07:20.993186+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6TPRTSRLKMA","created_at":"2026-07-05T11:07:20.993186+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6TPRTSRLKMADQDX","created_at":"2026-07-05T11:07:20.993186+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6TPRTSR","created_at":"2026-07-05T11:07:20.993186+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/L6TPRTSRLKMADQDXYMW4QYI7WW","json":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW.json","graph_json":"https://pith.science/api/pith-number/L6TPRTSRLKMADQDXYMW4QYI7WW/graph.json","events_json":"https://pith.science/api/pith-number/L6TPRTSRLKMADQDXYMW4QYI7WW/events.json","paper":"https://pith.science/paper/L6TPRTSR"},"agent_actions":{"view_html":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW","download_json":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW.json","view_paper":"https://pith.science/paper/L6TPRTSR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.18225&json=true","fetch_graph":"https://pith.science/api/pith-number/L6TPRTSRLKMADQDXYMW4QYI7WW/graph.json","fetch_events":"https://pith.science/api/pith-number/L6TPRTSRLKMADQDXYMW4QYI7WW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW/action/storage_attestation","attest_author":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW/action/author_attestation","sign_citation":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW/action/citation_signature","submit_replication":"https://pith.science/pith/L6TPRTSRLKMADQDXYMW4QYI7WW/action/replication_record"}},"created_at":"2026-07-05T11:07:20.993186+00:00","updated_at":"2026-07-05T11:07:20.993186+00:00"}