{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XKXSXJRUGAQOFEV2RMPPGWEK6W","short_pith_number":"pith:XKXSXJRU","canonical_record":{"source":{"id":"1705.04267","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T16:32:55Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e8a594db6a82e24928148c10e02c4758fedd7e426442dee25040b0b7394deffa","abstract_canon_sha256":"1aa065892d4c3b1db3cec3f83e7ce152e5e21a66189c2c131e22e879891f417a"},"schema_version":"1.0"},"canonical_sha256":"baaf2ba6343020e292ba8b1ef3588af5a93a37da1de1250490c140c2d33e18ab","source":{"kind":"arxiv","id":"1705.04267","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.04267","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"arxiv_version","alias_value":"1705.04267v2","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04267","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"pith_short_12","alias_value":"XKXSXJRUGAQO","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XKXSXJRUGAQOFEV2","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XKXSXJRU","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XKXSXJRUGAQOFEV2RMPPGWEK6W","target":"record","payload":{"canonical_record":{"source":{"id":"1705.04267","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T16:32:55Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e8a594db6a82e24928148c10e02c4758fedd7e426442dee25040b0b7394deffa","abstract_canon_sha256":"1aa065892d4c3b1db3cec3f83e7ce152e5e21a66189c2c131e22e879891f417a"},"schema_version":"1.0"},"canonical_sha256":"baaf2ba6343020e292ba8b1ef3588af5a93a37da1de1250490c140c2d33e18ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:38.509656Z","signature_b64":"ZM/nztCANYahCGBkxJpmtCC3PovTTwNISwqILaZd1YXfR8LvaGpADheEMIqEB/XHYuDSnGl7rbvQWXPxT1BnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"baaf2ba6343020e292ba8b1ef3588af5a93a37da1de1250490c140c2d33e18ab","last_reissued_at":"2026-05-18T00:36:38.509005Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:38.509005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.04267","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":"2026-05-18T00:36:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kVG/7378FE14nsNAjiZj09/Ym+n7CA2DQwPyab3LYk/0P7c+c8qqYB5j5JCvC8JY7MM0ivjRKkY+2choSJxdCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:09:03.533596Z"},"content_sha256":"fa78682cead23843541e58f409911181249ab5cb4422b4f141d54faf84e357c9","schema_version":"1.0","event_id":"sha256:fa78682cead23843541e58f409911181249ab5cb4422b4f141d54faf84e357c9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XKXSXJRUGAQOFEV2RMPPGWEK6W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Dufan Wu, Georges El Fakhri, Kyungsang Kim, Quanzheng Li","submitted_at":"2017-05-11T16:32:55Z","abstract_excerpt":"Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in the denoised image due to complexity of noises. A cascaded training network was proposed in this work, where the trained CNN was applied on the training dataset to initiate new trainings and remove artifacts induced by denoising. A cascades of convolutional neural networks (CNN)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04267","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":""},"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":"2026-05-18T00:36:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kQEkxxGngmChJNH/6qL73b2aTTmR0kRs5BZ2+JaOhEsij2BK4N04V1ey0NuhQizwHPJWHCjv4yE+Kf1tT+6oAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:09:03.534180Z"},"content_sha256":"161ce1ae92c57dd014323916478ebef3b082fd6e4f7ad0c25a81c6bed13a1616","schema_version":"1.0","event_id":"sha256:161ce1ae92c57dd014323916478ebef3b082fd6e4f7ad0c25a81c6bed13a1616"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W/bundle.json","state_url":"https://pith.science/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W/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":"2026-06-07T22:09:03Z","links":{"resolver":"https://pith.science/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W","bundle":"https://pith.science/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W/bundle.json","state":"https://pith.science/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XKXSXJRUGAQOFEV2RMPPGWEK6W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XKXSXJRUGAQOFEV2RMPPGWEK6W","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":"1aa065892d4c3b1db3cec3f83e7ce152e5e21a66189c2c131e22e879891f417a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T16:32:55Z","title_canon_sha256":"e8a594db6a82e24928148c10e02c4758fedd7e426442dee25040b0b7394deffa"},"schema_version":"1.0","source":{"id":"1705.04267","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.04267","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"arxiv_version","alias_value":"1705.04267v2","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04267","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"pith_short_12","alias_value":"XKXSXJRUGAQO","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XKXSXJRUGAQOFEV2","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XKXSXJRU","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:161ce1ae92c57dd014323916478ebef3b082fd6e4f7ad0c25a81c6bed13a1616","target":"graph","created_at":"2026-05-18T00:36:38Z","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":"Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in the denoised image due to complexity of noises. A cascaded training network was proposed in this work, where the trained CNN was applied on the training dataset to initiate new trainings and remove artifacts induced by denoising. A cascades of convolutional neural networks (CNN)","authors_text":"Dufan Wu, Georges El Fakhri, Kyungsang Kim, Quanzheng Li","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T16:32:55Z","title":"A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04267","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:fa78682cead23843541e58f409911181249ab5cb4422b4f141d54faf84e357c9","target":"record","created_at":"2026-05-18T00:36:38Z","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":"1aa065892d4c3b1db3cec3f83e7ce152e5e21a66189c2c131e22e879891f417a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T16:32:55Z","title_canon_sha256":"e8a594db6a82e24928148c10e02c4758fedd7e426442dee25040b0b7394deffa"},"schema_version":"1.0","source":{"id":"1705.04267","kind":"arxiv","version":2}},"canonical_sha256":"baaf2ba6343020e292ba8b1ef3588af5a93a37da1de1250490c140c2d33e18ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"baaf2ba6343020e292ba8b1ef3588af5a93a37da1de1250490c140c2d33e18ab","first_computed_at":"2026-05-18T00:36:38.509005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:38.509005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZM/nztCANYahCGBkxJpmtCC3PovTTwNISwqILaZd1YXfR8LvaGpADheEMIqEB/XHYuDSnGl7rbvQWXPxT1BnDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:38.509656Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.04267","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa78682cead23843541e58f409911181249ab5cb4422b4f141d54faf84e357c9","sha256:161ce1ae92c57dd014323916478ebef3b082fd6e4f7ad0c25a81c6bed13a1616"],"state_sha256":"4c04512bc0f4ee3407bd1c797c384c2dd3edf12e9a3688752a8b30103489b4d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZK7mW4Nd3fWSYSFu1jzCnqUqNSjFRP3jepG3630ViclU7K0bMXKDGtTlqSjbtrSw2Pag2Qjup8nfx+hst93EDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T22:09:03.537533Z","bundle_sha256":"bb5e7d4435b1f2c05a638e634589bccd0fdd1b3e5ac1411d61ffa10fce6efcb9"}}