{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AYRTWJ6RJD6JCBBBF4YESXDSTY","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":"bfb17b3956c3180e4bbf492f7c0d83eadac7b65f7cd0207bc5cd506e44f372a1","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-11-19T18:22:25Z","title_canon_sha256":"db3db02eab9cdd4649ca0aeb03aad940bd0338e2d88aeeac1a7aafaf76cb0ff4"},"schema_version":"1.0","source":{"id":"2411.12707","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.12707","created_at":"2026-07-05T09:37:47Z"},{"alias_kind":"arxiv_version","alias_value":"2411.12707v1","created_at":"2026-07-05T09:37:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12707","created_at":"2026-07-05T09:37:47Z"},{"alias_kind":"pith_short_12","alias_value":"AYRTWJ6RJD6J","created_at":"2026-07-05T09:37:47Z"},{"alias_kind":"pith_short_16","alias_value":"AYRTWJ6RJD6JCBBB","created_at":"2026-07-05T09:37:47Z"},{"alias_kind":"pith_short_8","alias_value":"AYRTWJ6R","created_at":"2026-07-05T09:37:47Z"}],"graph_snapshots":[{"event_id":"sha256:06f0bf894ce94990b8c4af3646990eaea80b86a8d1491bfc95483b3e8d549dbf","target":"graph","created_at":"2026-07-05T09:37:47Z","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/2411.12707/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Imaging-based deep learning has transformed healthcare research, yet its clinical adoption remains limited due to challenges in comparing imaging models with traditional non-imaging and tabular data. To bridge this gap, we introduce Barttender, an interpretable framework that uses deep learning for the direct comparison of the utility of imaging versus non-imaging tabular data for tasks like disease prediction.\n  Barttender converts non-imaging tabular features, such as scalar data from electronic health records, into grayscale bars, facilitating an interpretable and scalable deep learning bas","authors_text":"Ayush Singla, Chirag J. Patel, Shakson Isaac","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-11-19T18:22:25Z","title":"Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12707","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":"sha256:7fb8c7483762260c5528e402edaf2bee7f29e8bd35aeb3783c5d896cf979cbf6","target":"record","created_at":"2026-07-05T09:37:47Z","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":"bfb17b3956c3180e4bbf492f7c0d83eadac7b65f7cd0207bc5cd506e44f372a1","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-11-19T18:22:25Z","title_canon_sha256":"db3db02eab9cdd4649ca0aeb03aad940bd0338e2d88aeeac1a7aafaf76cb0ff4"},"schema_version":"1.0","source":{"id":"2411.12707","kind":"arxiv","version":1}},"canonical_sha256":"06233b27d148fc9104212f30495c729e0cbf0fbde3380320cce4ee804613f9d9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"06233b27d148fc9104212f30495c729e0cbf0fbde3380320cce4ee804613f9d9","first_computed_at":"2026-07-05T09:37:47.017341Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:37:47.017341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lKt7qQ/4/WyNEhNB3YiCdPuL8zhcWrL05gupMT7VAzRureDsKnzJR3bNa3XWzsOFcMMlkYfR4lybFzECVR1RBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:37:47.017751Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.12707","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7fb8c7483762260c5528e402edaf2bee7f29e8bd35aeb3783c5d896cf979cbf6","sha256:06f0bf894ce94990b8c4af3646990eaea80b86a8d1491bfc95483b3e8d549dbf"],"state_sha256":"1a425afb652409648c0df1fc274035dbe06ca721f226cf5dfb33c23f1bbb5fb9"}