{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:N2Y3U246L6TWJWUUYT2GBB7USB","short_pith_number":"pith:N2Y3U246","canonical_record":{"source":{"id":"1803.04565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-12T22:57:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"09340f3ebcbe65b4ec1fbf1b6a2006ff02fed5a2f348d4cb08dd6507ca6559e6","abstract_canon_sha256":"15a99e5c9e134a947c6a8a1bfa668bf03fe860ef4ee8d589f62533ca0398b7e0"},"schema_version":"1.0"},"canonical_sha256":"6eb1ba6b9e5fa764da94c4f46087f490560452e56fceb90af3330265520e6dcf","source":{"kind":"arxiv","id":"1803.04565","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.04565","created_at":"2026-05-18T00:21:18Z"},{"alias_kind":"arxiv_version","alias_value":"1803.04565v1","created_at":"2026-05-18T00:21:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.04565","created_at":"2026-05-18T00:21:18Z"},{"alias_kind":"pith_short_12","alias_value":"N2Y3U246L6TW","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"N2Y3U246L6TWJWUU","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"N2Y3U246","created_at":"2026-05-18T12:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:N2Y3U246L6TWJWUUYT2GBB7USB","target":"record","payload":{"canonical_record":{"source":{"id":"1803.04565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-12T22:57:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"09340f3ebcbe65b4ec1fbf1b6a2006ff02fed5a2f348d4cb08dd6507ca6559e6","abstract_canon_sha256":"15a99e5c9e134a947c6a8a1bfa668bf03fe860ef4ee8d589f62533ca0398b7e0"},"schema_version":"1.0"},"canonical_sha256":"6eb1ba6b9e5fa764da94c4f46087f490560452e56fceb90af3330265520e6dcf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:18.673093Z","signature_b64":"/fOPO1dOKeQaUvrHXJo/KvCrJN80VpcDLc85uP7ZdD8mwy8C6zXBu91xci/yJgL2IODUmbWvr3tkXxn5LD8JBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6eb1ba6b9e5fa764da94c4f46087f490560452e56fceb90af3330265520e6dcf","last_reissued_at":"2026-05-18T00:21:18.672600Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:18.672600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.04565","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:21:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ar4yTbBLj8tnS77ZrDt46LNPaH6dO8dYbY+HKrf5LyTR1T/ZMBAONIPC3lA1IFbgoiWBxsBkxYosq2awD+XhAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:50:09.138815Z"},"content_sha256":"f13c31532c06d0906174e1537cdd88d84840b334f83c5057d82d003cf670a642","schema_version":"1.0","event_id":"sha256:f13c31532c06d0906174e1537cdd88d84840b334f83c5057d82d003cf670a642"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:N2Y3U246L6TWJWUUYT2GBB7USB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Andreas Meier, Bogdan Georgescu, Dorin Comaniciu, Kevin Zhou, Ludwig Ritschl, Sasa Grbic, Sebastian Guendel","submitted_at":"2018-03-12T22:57:18Z","abstract_excerpt":"Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.04565","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:21:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ptInaUGaJLQxuiFxl1Xfz8EuSob/D1Fvqz38cj2sDo9y/jFqkZ4MzOWClOSEFyJa6SRUjXaG73Ebgg48fwL/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:50:09.139502Z"},"content_sha256":"76f2b72d80c831e4b4aa688b99dca724949e6f8eafd99019666681a53e9bb578","schema_version":"1.0","event_id":"sha256:76f2b72d80c831e4b4aa688b99dca724949e6f8eafd99019666681a53e9bb578"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N2Y3U246L6TWJWUUYT2GBB7USB/bundle.json","state_url":"https://pith.science/pith/N2Y3U246L6TWJWUUYT2GBB7USB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N2Y3U246L6TWJWUUYT2GBB7USB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-27T22:50:09Z","links":{"resolver":"https://pith.science/pith/N2Y3U246L6TWJWUUYT2GBB7USB","bundle":"https://pith.science/pith/N2Y3U246L6TWJWUUYT2GBB7USB/bundle.json","state":"https://pith.science/pith/N2Y3U246L6TWJWUUYT2GBB7USB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N2Y3U246L6TWJWUUYT2GBB7USB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:N2Y3U246L6TWJWUUYT2GBB7USB","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":"15a99e5c9e134a947c6a8a1bfa668bf03fe860ef4ee8d589f62533ca0398b7e0","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-12T22:57:18Z","title_canon_sha256":"09340f3ebcbe65b4ec1fbf1b6a2006ff02fed5a2f348d4cb08dd6507ca6559e6"},"schema_version":"1.0","source":{"id":"1803.04565","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.04565","created_at":"2026-05-18T00:21:18Z"},{"alias_kind":"arxiv_version","alias_value":"1803.04565v1","created_at":"2026-05-18T00:21:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.04565","created_at":"2026-05-18T00:21:18Z"},{"alias_kind":"pith_short_12","alias_value":"N2Y3U246L6TW","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"N2Y3U246L6TWJWUU","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"N2Y3U246","created_at":"2026-05-18T12:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:76f2b72d80c831e4b4aa688b99dca724949e6f8eafd99019666681a53e9bb578","target":"graph","created_at":"2026-05-18T00:21:18Z","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"},"paper":{"abstract_excerpt":"Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information","authors_text":"Andreas Meier, Bogdan Georgescu, Dorin Comaniciu, Kevin Zhou, Ludwig Ritschl, Sasa Grbic, Sebastian Guendel","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-12T22:57:18Z","title":"Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.04565","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:f13c31532c06d0906174e1537cdd88d84840b334f83c5057d82d003cf670a642","target":"record","created_at":"2026-05-18T00:21:18Z","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":"15a99e5c9e134a947c6a8a1bfa668bf03fe860ef4ee8d589f62533ca0398b7e0","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-12T22:57:18Z","title_canon_sha256":"09340f3ebcbe65b4ec1fbf1b6a2006ff02fed5a2f348d4cb08dd6507ca6559e6"},"schema_version":"1.0","source":{"id":"1803.04565","kind":"arxiv","version":1}},"canonical_sha256":"6eb1ba6b9e5fa764da94c4f46087f490560452e56fceb90af3330265520e6dcf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6eb1ba6b9e5fa764da94c4f46087f490560452e56fceb90af3330265520e6dcf","first_computed_at":"2026-05-18T00:21:18.672600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:18.672600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/fOPO1dOKeQaUvrHXJo/KvCrJN80VpcDLc85uP7ZdD8mwy8C6zXBu91xci/yJgL2IODUmbWvr3tkXxn5LD8JBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:18.673093Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.04565","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f13c31532c06d0906174e1537cdd88d84840b334f83c5057d82d003cf670a642","sha256:76f2b72d80c831e4b4aa688b99dca724949e6f8eafd99019666681a53e9bb578"],"state_sha256":"b9a118c07734fc93e2c327e7da73b4dcf32385102a9f6bdf53c20e9a432dd952"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wVS3iNIBoKC3JzHckD8GABhfpLmZvsC6LYPZY26jKk+E5vB+O99AdVZGZx4KxXMsg8wwe/M7oENMACuf7eUaBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T22:50:09.143157Z","bundle_sha256":"316568992a8144e936f5b83f4bd490876a54c0847d8cb05bd291af219d87d5a3"}}