{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:62L4UF56RC75PBL26554HNL674","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":"40ef0e29ba777685d41b060c3db632d8e59137bda58c09dc0b05a223783c1f30","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-28T14:50:50Z","title_canon_sha256":"a33fa7f2bcfd2e019d0826487aec169d729a3cbd89c2f864a9b3e59118d2e127"},"schema_version":"1.0","source":{"id":"2409.19371","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.19371","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.19371v1","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19371","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"62L4UF56RC75","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"62L4UF56RC75PBL2","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"62L4UF56","created_at":"2026-07-05T09:13:14Z"}],"graph_snapshots":[{"event_id":"sha256:f184d8a61a93e68f9e6ea5a2fb86168e83579a46b72660fde4a8d820ba2fa918","target":"graph","created_at":"2026-07-05T09:13:14Z","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/2409.19371/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propose novel $\\Gamma$-distribution Latent Denoising Diffusion Models (LDMs) designed to generate semantically guided synthetic cardiac ultrasound images with improved computational efficiency. We also investigate the potential of using these synthetic images as a replacement for real data in training deep networks for left-ventricular segmentation and binary echocardiogram view classification tasks. We compared six diffus","authors_text":"Alberto Gomez, Arian Beqiri, David Stojanovski, Mariana da Silva, Pablo Lamata","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-28T14:50:50Z","title":"Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19371","kind":"arxiv","version":1},"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:b1a866f6cba6bcd43df6bb120227e13375f02f283c673d6c8a56a15a0dee0cfc","target":"record","created_at":"2026-07-05T09:13:14Z","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":"40ef0e29ba777685d41b060c3db632d8e59137bda58c09dc0b05a223783c1f30","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-28T14:50:50Z","title_canon_sha256":"a33fa7f2bcfd2e019d0826487aec169d729a3cbd89c2f864a9b3e59118d2e127"},"schema_version":"1.0","source":{"id":"2409.19371","kind":"arxiv","version":1}},"canonical_sha256":"f697ca17be88bfd7857af77bc3b57eff3bbbd2daef706179fe5f15ff9afa1dba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f697ca17be88bfd7857af77bc3b57eff3bbbd2daef706179fe5f15ff9afa1dba","first_computed_at":"2026-07-05T09:13:14.480187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:13:14.480187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hL6/tm03BviwTLrFDqFtIF5fOVHSP1ICauabQUzXjpNcA2U4WjLOOsUkTtrcK1wsgHaIU9kpD6b6A4IeR9NbCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:13:14.480656Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.19371","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1a866f6cba6bcd43df6bb120227e13375f02f283c673d6c8a56a15a0dee0cfc","sha256:f184d8a61a93e68f9e6ea5a2fb86168e83579a46b72660fde4a8d820ba2fa918"],"state_sha256":"2b3415bd490b6c56615a196ed1d54388c8f9728642f411812125d2035cbeeccc"}