{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RLNG4N7C24UH4SFTSYO3XSLLMJ","short_pith_number":"pith:RLNG4N7C","canonical_record":{"source":{"id":"2212.10939","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-21T11:28:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67a81a679ce7a6fe64533a7c98530aa0cc4b013d5f6acf41928f55eddd66a3db","abstract_canon_sha256":"f30b0426204594a0542078a820f10816a6457d9e6bf767c51ec2726563bd281d"},"schema_version":"1.0"},"canonical_sha256":"8ada6e37e2d7287e48b3961dbbc96b627debb98b12e78e7f37f9b92fae996532","source":{"kind":"arxiv","id":"2212.10939","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10939","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10939v2","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10939","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"pith_short_12","alias_value":"RLNG4N7C24UH","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"pith_short_16","alias_value":"RLNG4N7C24UH4SFT","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"pith_short_8","alias_value":"RLNG4N7C","created_at":"2026-07-05T05:27:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RLNG4N7C24UH4SFTSYO3XSLLMJ","target":"record","payload":{"canonical_record":{"source":{"id":"2212.10939","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-21T11:28:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67a81a679ce7a6fe64533a7c98530aa0cc4b013d5f6acf41928f55eddd66a3db","abstract_canon_sha256":"f30b0426204594a0542078a820f10816a6457d9e6bf767c51ec2726563bd281d"},"schema_version":"1.0"},"canonical_sha256":"8ada6e37e2d7287e48b3961dbbc96b627debb98b12e78e7f37f9b92fae996532","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:50.037886Z","signature_b64":"oZEveW2Nrl8lSdXEsjcmEIoXlIs4uxNr71ts+8kSHXsSHfToHMBiJKnsXrYqfK9YBWXuv/OXl3cKJwHcNdQ2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ada6e37e2d7287e48b3961dbbc96b627debb98b12e78e7f37f9b92fae996532","last_reissued_at":"2026-07-05T05:27:50.037412Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:50.037412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.10939","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:27:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kThG2Tk8qkhFerBjGB2flTU7s6jAIfUJUovYW6WTsvVAtzAZ2T7ko7+potHOcsdNoocFvluhLopDLVY/lL3KCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T19:56:42.733866Z"},"content_sha256":"0de90b7202fe9086405da0049bb3c496211b06617cc808b0215819a7843ed199","schema_version":"1.0","event_id":"sha256:0de90b7202fe9086405da0049bb3c496211b06617cc808b0215819a7843ed199"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RLNG4N7C24UH4SFTSYO3XSLLMJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Inha Kang, Jinah Park","submitted_at":"2022-12-21T11:28:52Z","abstract_excerpt":"Obtaining ground truth data in medical imaging has difficulties due to the fact that it requires a lot of annotating time from the experts in the field. Also, when trained with supervised learning, it detects only the cases included in the labels. In real practice, we want to also open to other possibilities than the named cases while examining the medical images. As a solution, the need for anomaly detection that can detect and localize abnormalities by learning the normal characteristics using only normal images is emerging. With medical image data, we can design either 2D or 3D networks of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10939","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.10939/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T05:27:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KXx6aEQrNOhqtRTtc10wPbf0MOW+POte7zSpfkGRwCSmtWgHljQ2ZD5Euz/v567Zhv5fKL5BKj7Eq6v/BhC+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T19:56:42.734239Z"},"content_sha256":"99e5865ba502d68a71f64c6c64f0848e5bc809b45757e9ce0c1b7925e3c3c603","schema_version":"1.0","event_id":"sha256:99e5865ba502d68a71f64c6c64f0848e5bc809b45757e9ce0c1b7925e3c3c603"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ/bundle.json","state_url":"https://pith.science/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ/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":"2026-07-18T19:56:42Z","links":{"resolver":"https://pith.science/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ","bundle":"https://pith.science/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ/bundle.json","state":"https://pith.science/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RLNG4N7C24UH4SFTSYO3XSLLMJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RLNG4N7C24UH4SFTSYO3XSLLMJ","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":"f30b0426204594a0542078a820f10816a6457d9e6bf767c51ec2726563bd281d","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-21T11:28:52Z","title_canon_sha256":"67a81a679ce7a6fe64533a7c98530aa0cc4b013d5f6acf41928f55eddd66a3db"},"schema_version":"1.0","source":{"id":"2212.10939","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10939","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10939v2","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10939","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"pith_short_12","alias_value":"RLNG4N7C24UH","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"pith_short_16","alias_value":"RLNG4N7C24UH4SFT","created_at":"2026-07-05T05:27:50Z"},{"alias_kind":"pith_short_8","alias_value":"RLNG4N7C","created_at":"2026-07-05T05:27:50Z"}],"graph_snapshots":[{"event_id":"sha256:99e5865ba502d68a71f64c6c64f0848e5bc809b45757e9ce0c1b7925e3c3c603","target":"graph","created_at":"2026-07-05T05:27:50Z","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/2212.10939/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Obtaining ground truth data in medical imaging has difficulties due to the fact that it requires a lot of annotating time from the experts in the field. Also, when trained with supervised learning, it detects only the cases included in the labels. In real practice, we want to also open to other possibilities than the named cases while examining the medical images. As a solution, the need for anomaly detection that can detect and localize abnormalities by learning the normal characteristics using only normal images is emerging. With medical image data, we can design either 2D or 3D networks of ","authors_text":"Inha Kang, Jinah Park","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-21T11:28:52Z","title":"Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10939","kind":"arxiv","version":2},"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":"sha256:0de90b7202fe9086405da0049bb3c496211b06617cc808b0215819a7843ed199","target":"record","created_at":"2026-07-05T05:27:50Z","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":"f30b0426204594a0542078a820f10816a6457d9e6bf767c51ec2726563bd281d","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-21T11:28:52Z","title_canon_sha256":"67a81a679ce7a6fe64533a7c98530aa0cc4b013d5f6acf41928f55eddd66a3db"},"schema_version":"1.0","source":{"id":"2212.10939","kind":"arxiv","version":2}},"canonical_sha256":"8ada6e37e2d7287e48b3961dbbc96b627debb98b12e78e7f37f9b92fae996532","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ada6e37e2d7287e48b3961dbbc96b627debb98b12e78e7f37f9b92fae996532","first_computed_at":"2026-07-05T05:27:50.037412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:27:50.037412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oZEveW2Nrl8lSdXEsjcmEIoXlIs4uxNr71ts+8kSHXsSHfToHMBiJKnsXrYqfK9YBWXuv/OXl3cKJwHcNdQ2CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:27:50.037886Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.10939","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0de90b7202fe9086405da0049bb3c496211b06617cc808b0215819a7843ed199","sha256:99e5865ba502d68a71f64c6c64f0848e5bc809b45757e9ce0c1b7925e3c3c603"],"state_sha256":"977212593a82c4e0f9f9c16ea330f7f31175525d39d95b5927271b59da9c34ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KGaGxQq+Jpfngjcbs9S642/EDokQ46TYLbnDNa9zlgwxBZGQm6wHxwR8XUd3zKiYjCB+xIG9OpPwnMCwdJPQCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-18T19:56:42.736913Z","bundle_sha256":"639c0485e95c5d3f48ac3f43471700acc7b0419255b16c8dfff0987365933e72"}}