{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YG6ATY24TNRBPFHAWAXCJ2LU3D","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":"5a1cb7f814a94aacdf4d26c7737d3565d62bc66170c819687edf96a4c411b6fb","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-16T10:51:21Z","title_canon_sha256":"8f8267d6d7e249410b527e916a107057f6549fcb7638bd4072ee9f47c37ed5ec"},"schema_version":"1.0","source":{"id":"2407.11594","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.11594","created_at":"2026-07-05T08:44:26Z"},{"alias_kind":"arxiv_version","alias_value":"2407.11594v1","created_at":"2026-07-05T08:44:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11594","created_at":"2026-07-05T08:44:26Z"},{"alias_kind":"pith_short_12","alias_value":"YG6ATY24TNRB","created_at":"2026-07-05T08:44:26Z"},{"alias_kind":"pith_short_16","alias_value":"YG6ATY24TNRBPFHA","created_at":"2026-07-05T08:44:26Z"},{"alias_kind":"pith_short_8","alias_value":"YG6ATY24","created_at":"2026-07-05T08:44:26Z"}],"graph_snapshots":[{"event_id":"sha256:5e587d0e082e93ede1967baf2760854639a2abb654d71feb75c2774d87c680f3","target":"graph","created_at":"2026-07-05T08:44:26Z","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/2407.11594/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models (DMs) have emerged as powerful foundation models for a variety of tasks, with a large focus in synthetic image generation. However, their requirement of large annotated datasets for training limits their applicability in medical imaging, where datasets are typically smaller and sparsely annotated. We introduce DiNO-Diffusion, a self-supervised method for training latent diffusion models (LDMs) that conditions the generation process on image embeddings extracted from DiNO. By eliminating the reliance on annotations, our training leverages over 868k unlabelled images from public","authors_text":"Guillermo Jimenez-Perez, Javier Montalt-Tordera, Jens Hooge, Josef Cersovsky, Pedro Osorio, Sadegh Mohammadi, Steffen Vogler","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-16T10:51:21Z","title":"DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11594","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:c9c8d3a49a0ce87fffc4b577bf22af68ca867e171082eb4990552552280f2ab0","target":"record","created_at":"2026-07-05T08:44:26Z","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":"5a1cb7f814a94aacdf4d26c7737d3565d62bc66170c819687edf96a4c411b6fb","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-16T10:51:21Z","title_canon_sha256":"8f8267d6d7e249410b527e916a107057f6549fcb7638bd4072ee9f47c37ed5ec"},"schema_version":"1.0","source":{"id":"2407.11594","kind":"arxiv","version":1}},"canonical_sha256":"c1bc09e35c9b621794e0b02e24e974d8e6acbd7abd258d5c48745fa7bda39131","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1bc09e35c9b621794e0b02e24e974d8e6acbd7abd258d5c48745fa7bda39131","first_computed_at":"2026-07-05T08:44:26.655943Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:26.655943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xx/I21IaYMmWp4f3fveu7PdODsX2ZJ04qpNWSyBJ9rOWe3xg1sjKK+7aF7yHjk2gwpebNVQk7eITML4qcGSrCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:26.656433Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.11594","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9c8d3a49a0ce87fffc4b577bf22af68ca867e171082eb4990552552280f2ab0","sha256:5e587d0e082e93ede1967baf2760854639a2abb654d71feb75c2774d87c680f3"],"state_sha256":"85929792e0520ddcc798307ac3dc0f1da51752e092f2ce1285279306e49542ec"}