{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OUQNPAFPM23OFTXZNNOTFVSXOB","short_pith_number":"pith:OUQNPAFP","canonical_record":{"source":{"id":"2112.02164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-12-03T21:38:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c909fdd63fd41bc061a4247a2c05fd0b84214296f772ed06d130aade9306717e","abstract_canon_sha256":"333cae0fa312fad1144489b2766996f1680f4d1b39bb1b3b37fe8b1fa1dd549b"},"schema_version":"1.0"},"canonical_sha256":"7520d780af66b6e2cef96b5d32d657707f850b9ed3749d2276ae1d21c73408c1","source":{"kind":"arxiv","id":"2112.02164","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02164","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02164v1","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02164","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"pith_short_12","alias_value":"OUQNPAFPM23O","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"pith_short_16","alias_value":"OUQNPAFPM23OFTXZ","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"pith_short_8","alias_value":"OUQNPAFP","created_at":"2026-07-05T05:05:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OUQNPAFPM23OFTXZNNOTFVSXOB","target":"record","payload":{"canonical_record":{"source":{"id":"2112.02164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-12-03T21:38:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c909fdd63fd41bc061a4247a2c05fd0b84214296f772ed06d130aade9306717e","abstract_canon_sha256":"333cae0fa312fad1144489b2766996f1680f4d1b39bb1b3b37fe8b1fa1dd549b"},"schema_version":"1.0"},"canonical_sha256":"7520d780af66b6e2cef96b5d32d657707f850b9ed3749d2276ae1d21c73408c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:05:29.192231Z","signature_b64":"FC0lZFeGX0s+N2BZPSFvSimaeoHgkr0YSOp2Un700xBSVSq3pJLfeXyqyukObfV9dlqFd82iiq7vbfXVs/EYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7520d780af66b6e2cef96b5d32d657707f850b9ed3749d2276ae1d21c73408c1","last_reissued_at":"2026-07-05T05:05:29.191749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:05:29.191749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.02164","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":"2026-07-05T05:05:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j8R7vE/JB+SQYeT9nKdqZRtOZhjX5DoIDNBDtuyhnDLlL+9ioERI4+PkfCxvncc5FTECBw8utc6b/DE+d3MVDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T23:43:42.858587Z"},"content_sha256":"c979696e8d8bc8bf25f2ef71a5db7685c1b8f4d9beadb007029c25de644a0167","schema_version":"1.0","event_id":"sha256:c979696e8d8bc8bf25f2ef71a5db7685c1b8f4d9beadb007029c25de644a0167"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OUQNPAFPM23OFTXZNNOTFVSXOB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bridging the gap between prostate radiology and pathology through machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Arun Seetharaman, Christian A. Kunder, David S. Lim, Geoffrey A. Sonn, Han Lin Aung, Indrani Bhattacharya, James D. Brooks, Katherine J. To'o, Mirabela Rusu, Pejman Ghanouni, Richard E. Fan, Simon J. C. Soerensen, Wei Shao, Xingchen Liu","submitted_at":"2021-12-03T21:38:20Z","abstract_excerpt":"Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02164","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2112.02164/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":"2026-07-05T05:05:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1vABwQaAfhZ9xAGNTaDeZiaPpsdcjbjo7Dpf/aHvh0d01J7mHvvXonn00Orq7vjvdSKsgX7XWpf7fKHyKe/mBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T23:43:42.858953Z"},"content_sha256":"60d0fc0b73366514b2ba95e57b56f843b71268b3fbc54d16f2f349af7aff73c7","schema_version":"1.0","event_id":"sha256:60d0fc0b73366514b2ba95e57b56f843b71268b3fbc54d16f2f349af7aff73c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OUQNPAFPM23OFTXZNNOTFVSXOB/bundle.json","state_url":"https://pith.science/pith/OUQNPAFPM23OFTXZNNOTFVSXOB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OUQNPAFPM23OFTXZNNOTFVSXOB/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":"2026-07-21T23:43:42Z","links":{"resolver":"https://pith.science/pith/OUQNPAFPM23OFTXZNNOTFVSXOB","bundle":"https://pith.science/pith/OUQNPAFPM23OFTXZNNOTFVSXOB/bundle.json","state":"https://pith.science/pith/OUQNPAFPM23OFTXZNNOTFVSXOB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OUQNPAFPM23OFTXZNNOTFVSXOB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OUQNPAFPM23OFTXZNNOTFVSXOB","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":"333cae0fa312fad1144489b2766996f1680f4d1b39bb1b3b37fe8b1fa1dd549b","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-12-03T21:38:20Z","title_canon_sha256":"c909fdd63fd41bc061a4247a2c05fd0b84214296f772ed06d130aade9306717e"},"schema_version":"1.0","source":{"id":"2112.02164","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02164","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02164v1","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02164","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"pith_short_12","alias_value":"OUQNPAFPM23O","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"pith_short_16","alias_value":"OUQNPAFPM23OFTXZ","created_at":"2026-07-05T05:05:29Z"},{"alias_kind":"pith_short_8","alias_value":"OUQNPAFP","created_at":"2026-07-05T05:05:29Z"}],"graph_snapshots":[{"event_id":"sha256:60d0fc0b73366514b2ba95e57b56f843b71268b3fbc54d16f2f349af7aff73c7","target":"graph","created_at":"2026-07-05T05:05:29Z","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/2112.02164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we","authors_text":"Arun Seetharaman, Christian A. Kunder, David S. Lim, Geoffrey A. Sonn, Han Lin Aung, Indrani Bhattacharya, James D. Brooks, Katherine J. To'o, Mirabela Rusu, Pejman Ghanouni, Richard E. Fan, Simon J. C. Soerensen, Wei Shao, Xingchen Liu","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-12-03T21:38:20Z","title":"Bridging the gap between prostate radiology and pathology through machine learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02164","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:c979696e8d8bc8bf25f2ef71a5db7685c1b8f4d9beadb007029c25de644a0167","target":"record","created_at":"2026-07-05T05:05:29Z","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":"333cae0fa312fad1144489b2766996f1680f4d1b39bb1b3b37fe8b1fa1dd549b","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-12-03T21:38:20Z","title_canon_sha256":"c909fdd63fd41bc061a4247a2c05fd0b84214296f772ed06d130aade9306717e"},"schema_version":"1.0","source":{"id":"2112.02164","kind":"arxiv","version":1}},"canonical_sha256":"7520d780af66b6e2cef96b5d32d657707f850b9ed3749d2276ae1d21c73408c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7520d780af66b6e2cef96b5d32d657707f850b9ed3749d2276ae1d21c73408c1","first_computed_at":"2026-07-05T05:05:29.191749Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:05:29.191749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FC0lZFeGX0s+N2BZPSFvSimaeoHgkr0YSOp2Un700xBSVSq3pJLfeXyqyukObfV9dlqFd82iiq7vbfXVs/EYAA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:05:29.192231Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.02164","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c979696e8d8bc8bf25f2ef71a5db7685c1b8f4d9beadb007029c25de644a0167","sha256:60d0fc0b73366514b2ba95e57b56f843b71268b3fbc54d16f2f349af7aff73c7"],"state_sha256":"d5fc05d9502fc6d6eeb0ca2f7d4d619b0c3b659e2be1d139b48d235dd391a0a5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LHz0A8Y+makDIQC2Fcs7KuJ5a0DdRH0pD/oJNQ79C9ixkwR05OrQ5TiqJmg2zJjs+GpZcrDWjbG2xcaQ47PhDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T23:43:42.861562Z","bundle_sha256":"eec7597c3f6d25e2233d44d67b06395ac926d51ca1f543eaf177798d2a3954f7"}}