{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:K4FVOIRJ2JMIT4ZQY42WBLJXD2","short_pith_number":"pith:K4FVOIRJ","schema_version":"1.0","canonical_sha256":"570b572229d25889f330c73560ad371e8d1d2b272e8bd82b73ff3d6c8715302b","source":{"kind":"arxiv","id":"2409.08691","version":2},"attestation_state":"computed","paper":{"title":"Autoregressive Sequence Modeling for 3D Medical Image Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Churan Wang, Fandong Zhang, Fei Gao, Lixian Su, Siwen Wang, Yizhou Wang, Yizhou Yu","submitted_at":"2024-09-13T10:19:10Z","abstract_excerpt":"Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image "},"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":"2409.08691","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-13T10:19:10Z","cross_cats_sorted":[],"title_canon_sha256":"4fc71664d13d5c35993be3fc56ede3a455fd9122b0fa71a6252ab0e0ed5d1b51","abstract_canon_sha256":"27f102d7b18085b4fa48b446c354db9fe99332d23c932a51ecd516b42b35c4ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:00.596656Z","signature_b64":"HFdGz1cOjeIdCbtCRUyLCtoNsxaGb5SIc8PFHKQPN6i/TUaVfToAYKRB/0IRovrIaUdc4xH0r4Tf5Z1PhLauCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"570b572229d25889f330c73560ad371e8d1d2b272e8bd82b73ff3d6c8715302b","last_reissued_at":"2026-07-05T11:08:00.596153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:00.596153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Autoregressive Sequence Modeling for 3D Medical Image Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Churan Wang, Fandong Zhang, Fei Gao, Lixian Su, Siwen Wang, Yizhou Wang, Yizhou Yu","submitted_at":"2024-09-13T10:19:10Z","abstract_excerpt":"Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.08691","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/2409.08691/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":"2409.08691","created_at":"2026-07-05T11:08:00.596211+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.08691v2","created_at":"2026-07-05T11:08:00.596211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.08691","created_at":"2026-07-05T11:08:00.596211+00:00"},{"alias_kind":"pith_short_12","alias_value":"K4FVOIRJ2JMI","created_at":"2026-07-05T11:08:00.596211+00:00"},{"alias_kind":"pith_short_16","alias_value":"K4FVOIRJ2JMIT4ZQ","created_at":"2026-07-05T11:08:00.596211+00:00"},{"alias_kind":"pith_short_8","alias_value":"K4FVOIRJ","created_at":"2026-07-05T11:08:00.596211+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.09919","citing_title":"T-CACE: A Time-Conditioned Autoregressive Contrast Enhancement Multi-Task Framework for Contrast-Free Liver MRI Synthesis, Segmentation, and Diagnosis","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2","json":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2.json","graph_json":"https://pith.science/api/pith-number/K4FVOIRJ2JMIT4ZQY42WBLJXD2/graph.json","events_json":"https://pith.science/api/pith-number/K4FVOIRJ2JMIT4ZQY42WBLJXD2/events.json","paper":"https://pith.science/paper/K4FVOIRJ"},"agent_actions":{"view_html":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2","download_json":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2.json","view_paper":"https://pith.science/paper/K4FVOIRJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.08691&json=true","fetch_graph":"https://pith.science/api/pith-number/K4FVOIRJ2JMIT4ZQY42WBLJXD2/graph.json","fetch_events":"https://pith.science/api/pith-number/K4FVOIRJ2JMIT4ZQY42WBLJXD2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2/action/storage_attestation","attest_author":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2/action/author_attestation","sign_citation":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2/action/citation_signature","submit_replication":"https://pith.science/pith/K4FVOIRJ2JMIT4ZQY42WBLJXD2/action/replication_record"}},"created_at":"2026-07-05T11:08:00.596211+00:00","updated_at":"2026-07-05T11:08:00.596211+00:00"}