{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NX7VCPKETTCEVRUXQ5QJVZK4L3","short_pith_number":"pith:NX7VCPKE","schema_version":"1.0","canonical_sha256":"6dff513d449cc44ac69787609ae55c5ece0d8593ae6919d5ed98a36642c724ab","source":{"kind":"arxiv","id":"2305.16037","version":5},"attestation_state":"computed","paper":{"title":"GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alperen Tezcan, Anjany Sekuboyina, Ayse Gulnihan Simsek, Bjoern Menze, Chinmay Prabhakar, Christian Bluethgen, Enis Simsar, Furkan Almas, Hadrien Reynaud, Ibrahim Ethem Hamamci, Irem Dogan, Mehmet Kemal Ozdemir, Muhammed Furkan Dasdelen, Sarthak Pati, Sevval Nil Esirgun, Sezgin Er","submitted_at":"2023-05-25T13:16:39Z","abstract_excerpt":"GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. Without directly comparable methods in 3D medical imaging, we benchmarked GenerateCT against cutting-edge methods, demonstrating its superiority across all key metrics. Importantly, we evaluated GenerateCT's clinical applications in a multi-abnormality class"},"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":"2305.16037","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T13:16:39Z","cross_cats_sorted":[],"title_canon_sha256":"0df9111a1a3539046f3e9a673796ef435a1540061ea035d155bd89f277bde62c","abstract_canon_sha256":"2bf4a1e6a00c91fc06251ca60c649e55d8c15f742d3e9a2c916fee2a1707ea0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:04.990239Z","signature_b64":"mdYqMFV9YZwxZk0VNDoL+PVQfrcA5HR0HxKchYQQ+X+fN2rSETZkdbuSwSMzf5Gvd0eCfL6CwPmQ2M4hG1fLCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6dff513d449cc44ac69787609ae55c5ece0d8593ae6919d5ed98a36642c724ab","last_reissued_at":"2026-07-05T08:43:04.989768Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:04.989768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alperen Tezcan, Anjany Sekuboyina, Ayse Gulnihan Simsek, Bjoern Menze, Chinmay Prabhakar, Christian Bluethgen, Enis Simsar, Furkan Almas, Hadrien Reynaud, Ibrahim Ethem Hamamci, Irem Dogan, Mehmet Kemal Ozdemir, Muhammed Furkan Dasdelen, Sarthak Pati, Sevval Nil Esirgun, Sezgin Er","submitted_at":"2023-05-25T13:16:39Z","abstract_excerpt":"GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. Without directly comparable methods in 3D medical imaging, we benchmarked GenerateCT against cutting-edge methods, demonstrating its superiority across all key metrics. Importantly, we evaluated GenerateCT's clinical applications in a multi-abnormality class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16037","kind":"arxiv","version":5},"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/2305.16037/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":"2305.16037","created_at":"2026-07-05T08:43:04.989821+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.16037v5","created_at":"2026-07-05T08:43:04.989821+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16037","created_at":"2026-07-05T08:43:04.989821+00:00"},{"alias_kind":"pith_short_12","alias_value":"NX7VCPKETTCE","created_at":"2026-07-05T08:43:04.989821+00:00"},{"alias_kind":"pith_short_16","alias_value":"NX7VCPKETTCEVRUX","created_at":"2026-07-05T08:43:04.989821+00:00"},{"alias_kind":"pith_short_8","alias_value":"NX7VCPKE","created_at":"2026-07-05T08:43:04.989821+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/NX7VCPKETTCEVRUXQ5QJVZK4L3","json":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3.json","graph_json":"https://pith.science/api/pith-number/NX7VCPKETTCEVRUXQ5QJVZK4L3/graph.json","events_json":"https://pith.science/api/pith-number/NX7VCPKETTCEVRUXQ5QJVZK4L3/events.json","paper":"https://pith.science/paper/NX7VCPKE"},"agent_actions":{"view_html":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3","download_json":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3.json","view_paper":"https://pith.science/paper/NX7VCPKE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.16037&json=true","fetch_graph":"https://pith.science/api/pith-number/NX7VCPKETTCEVRUXQ5QJVZK4L3/graph.json","fetch_events":"https://pith.science/api/pith-number/NX7VCPKETTCEVRUXQ5QJVZK4L3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3/action/storage_attestation","attest_author":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3/action/author_attestation","sign_citation":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3/action/citation_signature","submit_replication":"https://pith.science/pith/NX7VCPKETTCEVRUXQ5QJVZK4L3/action/replication_record"}},"created_at":"2026-07-05T08:43:04.989821+00:00","updated_at":"2026-07-05T08:43:04.989821+00:00"}