{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DTIVEKKUVIDHDRABCQMLAIE5JS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a8116d984817b4cfefaa698441192fa1de08f00489a82ab2ce125822717a9bcc","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-03-28T15:41:43Z","title_canon_sha256":"776c511a1c0ebf9bb390a686aedc2ced7937fedfea54204145671917015b0b8f"},"schema_version":"1.0","source":{"id":"2403.19508","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19508","created_at":"2026-07-05T12:05:59Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19508v2","created_at":"2026-07-05T12:05:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19508","created_at":"2026-07-05T12:05:59Z"},{"alias_kind":"pith_short_12","alias_value":"DTIVEKKUVIDH","created_at":"2026-07-05T12:05:59Z"},{"alias_kind":"pith_short_16","alias_value":"DTIVEKKUVIDHDRAB","created_at":"2026-07-05T12:05:59Z"},{"alias_kind":"pith_short_8","alias_value":"DTIVEKKU","created_at":"2026-07-05T12:05:59Z"}],"graph_snapshots":[{"event_id":"sha256:debe6e644613243a1d6ee28309599a53862c92c1b3d72e5e9b25a70e4d44090a","target":"graph","created_at":"2026-07-05T12:05:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.19508/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While deep learning holds great promise for disease diagnosis and prognosis in cardiac magnetic resonance imaging, its progress is often constrained by highly imbalanced and biased training datasets. To address this issue, we propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data based on sensitive attributes such as sex, age, body mass index (BMI), and health condition. We adopt ControlNet based on a denoising diffusion probabilistic model to condition on text assembled from patient metadata and cardiac geometry derived from segmentation masks. ","authors_text":"Grzegorz Skorupko, Kaisar Kushibar, Karim Lekadir, Nay Aung, Polyxeni Gkontra, Richard Osuala, Steffen E Petersen, Vien Ngoc Dang, Zuzanna Szafranowska","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-03-28T15:41:43Z","title":"Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19508","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ae07c7908765401cc1c1f6b247c6cff20e94f676b3d8d3fe853a0720cc722c02","target":"record","created_at":"2026-07-05T12:05:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a8116d984817b4cfefaa698441192fa1de08f00489a82ab2ce125822717a9bcc","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-03-28T15:41:43Z","title_canon_sha256":"776c511a1c0ebf9bb390a686aedc2ced7937fedfea54204145671917015b0b8f"},"schema_version":"1.0","source":{"id":"2403.19508","kind":"arxiv","version":2}},"canonical_sha256":"1cd1522954aa0671c4011418b0209d4cbe2583ad8b7b4477f3ea5fd893d5fea2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1cd1522954aa0671c4011418b0209d4cbe2583ad8b7b4477f3ea5fd893d5fea2","first_computed_at":"2026-07-05T12:05:59.665460Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:59.665460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4JCSEWUhPFytQ2MqCekztKbKTJHREFe0lMyXq0zFwr+l0UvV/lZls2M2itQVov+4+I6gKddKKg0kawaCFhT5Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:59.665982Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.19508","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae07c7908765401cc1c1f6b247c6cff20e94f676b3d8d3fe853a0720cc722c02","sha256:debe6e644613243a1d6ee28309599a53862c92c1b3d72e5e9b25a70e4d44090a"],"state_sha256":"cb0430aaf61c04a6c14c968f99603915d56f9daddc094a58ef210cd7182b3c90"}