{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ALWDCEDUS4AB667R6P6OTQJD5F","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":"19d9cafbf4cc422bfbf2eea068d7a9ece0485e24b4d3f991138f28ecf1778ac0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-25T23:46:48Z","title_canon_sha256":"310378b518f2f7173718dfa5170c39c658cd4f156a3ebeab41765a2d19904be4"},"schema_version":"1.0","source":{"id":"2307.13865","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.13865","created_at":"2026-07-05T06:52:06Z"},{"alias_kind":"arxiv_version","alias_value":"2307.13865v1","created_at":"2026-07-05T06:52:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.13865","created_at":"2026-07-05T06:52:06Z"},{"alias_kind":"pith_short_12","alias_value":"ALWDCEDUS4AB","created_at":"2026-07-05T06:52:06Z"},{"alias_kind":"pith_short_16","alias_value":"ALWDCEDUS4AB667R","created_at":"2026-07-05T06:52:06Z"},{"alias_kind":"pith_short_8","alias_value":"ALWDCEDU","created_at":"2026-07-05T06:52:06Z"}],"graph_snapshots":[{"event_id":"sha256:cb95d3c84b4beca66277e3b525681bdf63b8953e72ba721702199b0b5cbd85df","target":"graph","created_at":"2026-07-05T06:52:06Z","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/2307.13865/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presents significant challenges, both in terms of computational resources and data requirements. Moreover, achieving high-quality pretraining of 3D models proves to be even more challenging. To address these issues, hybrid 2.5D approaches provide an effective solution for utilizing 3D volumetric data efficiently using 2D models. Combining 2D and 3D techniques offers a promising avenue for optimizing performance while minim","authors_text":"Andrew Lotery, Antoine Rivail, Arunava Chakravarty, Daniel Rueckert, Hendrik P.N. Scholl, Hrvoje Bogunovi\\'c, Julia Mai, Marzieh Oghbaie, Sobha Sivaprasad, Sophie Riedl, Taha Emre, Ursula Schmidt-Erfurth","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-25T23:46:48Z","title":"Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.13865","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":"sha256:6a040479f992fb0e2f9cfa7dddc9d82c888efe5c4f32666c0ab47568cddd421b","target":"record","created_at":"2026-07-05T06:52:06Z","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":"19d9cafbf4cc422bfbf2eea068d7a9ece0485e24b4d3f991138f28ecf1778ac0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-25T23:46:48Z","title_canon_sha256":"310378b518f2f7173718dfa5170c39c658cd4f156a3ebeab41765a2d19904be4"},"schema_version":"1.0","source":{"id":"2307.13865","kind":"arxiv","version":1}},"canonical_sha256":"02ec31107497001f7bf1f3fce9c123e94298ac0442c228bdea483bc797976390","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02ec31107497001f7bf1f3fce9c123e94298ac0442c228bdea483bc797976390","first_computed_at":"2026-07-05T06:52:06.404667Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:52:06.404667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ia13zhqRzL6dUl7fspZlRqCn39OLsGOF4STKACLTz7sErvJ9ezRiYUBcEJS7xQhhjfju5Cv2QQX38y8BDKCXAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:52:06.405061Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.13865","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6a040479f992fb0e2f9cfa7dddc9d82c888efe5c4f32666c0ab47568cddd421b","sha256:cb95d3c84b4beca66277e3b525681bdf63b8953e72ba721702199b0b5cbd85df"],"state_sha256":"b598c59d62a3b038139536ea123e2b53140192b4b977415e2fa578502c136dfc"}