{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:D6HWI56UKUZP2Z6UABP57RE26S","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":"1c27b4a56b48ed3cf96a632b6fde21d636f7c89cb68dc3fc6cf8f930b3fc49b6","cross_cats_sorted":["eess.IV","q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-07T17:30:43Z","title_canon_sha256":"2373b824b2793fc181ccccc08f9d5671cfc38aae3b28c140d5596bc90db3ca31"},"schema_version":"1.0","source":{"id":"2206.03461","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03461","created_at":"2026-07-05T04:29:42Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03461v1","created_at":"2026-07-05T04:29:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03461","created_at":"2026-07-05T04:29:42Z"},{"alias_kind":"pith_short_12","alias_value":"D6HWI56UKUZP","created_at":"2026-07-05T04:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"D6HWI56UKUZP2Z6U","created_at":"2026-07-05T04:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"D6HWI56U","created_at":"2026-07-05T04:29:42Z"}],"graph_snapshots":[{"event_id":"sha256:cc6be33c8b3bf315d55f33c4015b5ffcfb124f677ac387bbbd53bfae74d78f1f","target":"graph","created_at":"2026-07-05T04:29:42Z","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/2206.03461/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generativ","authors_text":"Andrew D. MacKinnon, David Werring, Geraint Rees, James T. Teo, Mark S. Graham, M. Jorge Cardoso, Parashkev Nachev, Paul Wright, Pedro F Da Costa, Petru-Daniel Tudosiu, Robert Gray, Rolf Jager, Sebastien Ourselin, Walter H. L. Pinaya, Yee H. Mah","cross_cats":["eess.IV","q-bio.QM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-07T17:30:43Z","title":"Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03461","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":"sha256:94b5f10fe7862aaedb7812073b9623608760122a076003e4c8a166b32fad7b01","target":"record","created_at":"2026-07-05T04:29:42Z","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":"1c27b4a56b48ed3cf96a632b6fde21d636f7c89cb68dc3fc6cf8f930b3fc49b6","cross_cats_sorted":["eess.IV","q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-07T17:30:43Z","title_canon_sha256":"2373b824b2793fc181ccccc08f9d5671cfc38aae3b28c140d5596bc90db3ca31"},"schema_version":"1.0","source":{"id":"2206.03461","kind":"arxiv","version":1}},"canonical_sha256":"1f8f6477d45532fd67d4005fdfc49af48c18e74b275d3960e456c8f9d1608ce0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f8f6477d45532fd67d4005fdfc49af48c18e74b275d3960e456c8f9d1608ce0","first_computed_at":"2026-07-05T04:29:42.855137Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:29:42.855137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EYqKmldbc8yQl+xXR5x7LkzK8izpfpqi8m6V0Uq6GVpe1kZyrmKLB9cFkHwlhKKTU3CppiByIhe8Gqif9rrkBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:29:42.855679Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.03461","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94b5f10fe7862aaedb7812073b9623608760122a076003e4c8a166b32fad7b01","sha256:cc6be33c8b3bf315d55f33c4015b5ffcfb124f677ac387bbbd53bfae74d78f1f"],"state_sha256":"bda84e11810e6dab80503d1d5fd425459fa1c8e811bfbb1b2a96f8d2a47bf02f"}