{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DDJGAET62IAZKCNLTWQP7NTP6F","short_pith_number":"pith:DDJGAET6","schema_version":"1.0","canonical_sha256":"18d260127ed2019509ab9da0ffb66ff156d57027f8dfcc6be3246253ec953b10","source":{"kind":"arxiv","id":"2107.01748","version":1},"attestation_state":"computed","paper":{"title":"Controllable cardiac synthesis via disentangled anatomy arithmetic","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alison O'Neil, Sotirios A. Tsaftaris, Spyridon Thermos, Xiao Liu","submitted_at":"2021-07-04T23:13:33Z","abstract_excerpt":"Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper latent representation, the idea of a \"latent vector arithmetic\" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (ve"},"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":"2107.01748","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-07-04T23:13:33Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a4e39098a3192b0a7f874667461232a80f1767a88031ee72a028950077fa4ab5","abstract_canon_sha256":"a225e1403d3cd7f506263cdd7e533546ded0721ce4b220bb7f190597b098ab40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:55:00.379934Z","signature_b64":"Nkh4bFzdUN5QDhfEcnlDJxLcg3+wvapZ+HktonoqAQ+OSsjo3dJZLsMiXRqvYEbUhg+AdhomBBrtcSo+FKqTBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"18d260127ed2019509ab9da0ffb66ff156d57027f8dfcc6be3246253ec953b10","last_reissued_at":"2026-07-05T02:55:00.379541Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:55:00.379541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Controllable cardiac synthesis via disentangled anatomy arithmetic","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alison O'Neil, Sotirios A. Tsaftaris, Spyridon Thermos, Xiao Liu","submitted_at":"2021-07-04T23:13:33Z","abstract_excerpt":"Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper latent representation, the idea of a \"latent vector arithmetic\" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (ve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.01748","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.01748/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":"2107.01748","created_at":"2026-07-05T02:55:00.379603+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.01748v1","created_at":"2026-07-05T02:55:00.379603+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.01748","created_at":"2026-07-05T02:55:00.379603+00:00"},{"alias_kind":"pith_short_12","alias_value":"DDJGAET62IAZ","created_at":"2026-07-05T02:55:00.379603+00:00"},{"alias_kind":"pith_short_16","alias_value":"DDJGAET62IAZKCNL","created_at":"2026-07-05T02:55:00.379603+00:00"},{"alias_kind":"pith_short_8","alias_value":"DDJGAET6","created_at":"2026-07-05T02:55:00.379603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.06327","citing_title":"Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F","json":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F.json","graph_json":"https://pith.science/api/pith-number/DDJGAET62IAZKCNLTWQP7NTP6F/graph.json","events_json":"https://pith.science/api/pith-number/DDJGAET62IAZKCNLTWQP7NTP6F/events.json","paper":"https://pith.science/paper/DDJGAET6"},"agent_actions":{"view_html":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F","download_json":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F.json","view_paper":"https://pith.science/paper/DDJGAET6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.01748&json=true","fetch_graph":"https://pith.science/api/pith-number/DDJGAET62IAZKCNLTWQP7NTP6F/graph.json","fetch_events":"https://pith.science/api/pith-number/DDJGAET62IAZKCNLTWQP7NTP6F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F/action/storage_attestation","attest_author":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F/action/author_attestation","sign_citation":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F/action/citation_signature","submit_replication":"https://pith.science/pith/DDJGAET62IAZKCNLTWQP7NTP6F/action/replication_record"}},"created_at":"2026-07-05T02:55:00.379603+00:00","updated_at":"2026-07-05T02:55:00.379603+00:00"}