{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:F56FQMBKNQ2JQ7CWJUHRHGELFJ","short_pith_number":"pith:F56FQMBK","canonical_record":{"source":{"id":"1912.08679","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-12-18T15:56:29Z","cross_cats_sorted":["cs.CV","cs.LG","q-bio.QM"],"title_canon_sha256":"a91874d4a510ec1246a7e49ca50ae3dce4f1546c1b28ee06cae54a9e4e66131f","abstract_canon_sha256":"e690218e4e65e053967d357ce6675586f562f7812eadcc7a668d75b836cd8670"},"schema_version":"1.0"},"canonical_sha256":"2f7c58302a6c34987c564d0f13988b2a4d40e813acb28583d83a795128f8e8e5","source":{"kind":"arxiv","id":"1912.08679","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.08679","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"arxiv_version","alias_value":"1912.08679v1","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.08679","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"pith_short_12","alias_value":"F56FQMBKNQ2J","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"pith_short_16","alias_value":"F56FQMBKNQ2JQ7CW","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"pith_short_8","alias_value":"F56FQMBK","created_at":"2026-07-05T00:27:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:F56FQMBKNQ2JQ7CWJUHRHGELFJ","target":"record","payload":{"canonical_record":{"source":{"id":"1912.08679","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-12-18T15:56:29Z","cross_cats_sorted":["cs.CV","cs.LG","q-bio.QM"],"title_canon_sha256":"a91874d4a510ec1246a7e49ca50ae3dce4f1546c1b28ee06cae54a9e4e66131f","abstract_canon_sha256":"e690218e4e65e053967d357ce6675586f562f7812eadcc7a668d75b836cd8670"},"schema_version":"1.0"},"canonical_sha256":"2f7c58302a6c34987c564d0f13988b2a4d40e813acb28583d83a795128f8e8e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:27:06.163321Z","signature_b64":"rSCNoOes7HQW583WqicEatLNxhMDMc9T0lcIZCPsg/FBBeeMgsmAHlvoHgFBq2WKdqPkQdwwGVQQgFOW33JuBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f7c58302a6c34987c564d0f13988b2a4d40e813acb28583d83a795128f8e8e5","last_reissued_at":"2026-07-05T00:27:06.162822Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:27:06.162822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.08679","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":"2026-07-05T00:27:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jbhUn8JEU92vOBWUGw0Tz4MQzBJu6zzyZ2i511zYjokriHV5EQeoFDarfotm8aozqLaThDp8NoULvOqkpo6rCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:38:28.652634Z"},"content_sha256":"5f090e8d347842f879de2a8dce8744722c27148119bf74b973d368776b90f699","schema_version":"1.0","event_id":"sha256:5f090e8d347842f879de2a8dce8744722c27148119bf74b973d368776b90f699"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:F56FQMBKNQ2JQ7CWJUHRHGELFJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","q-bio.QM"],"primary_cat":"eess.IV","authors_text":"Gemma Piella, Ilaria Bonavita, Mario Ceresa, Miguel A. Gonz\\'alez Ballester, Vicent Ribas, Xavier Rafael-Palou","submitted_at":"2019-12-18T15:56:29Z","abstract_excerpt":"The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.08679","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/1912.08679/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":"2026-07-05T00:27:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HTjEMTMx/gbQoMwRp7ekEIcoOHIwEEVb4pzsWKYaoBQG0nB5MDcLN1icNa6Nv9J7Un0U14FUTlX2DoJWDZkpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:38:28.653663Z"},"content_sha256":"84179d72e80a762fec3d2ed2b2741ec9841a05f9c8bd3969ecf793c68d74f62d","schema_version":"1.0","event_id":"sha256:84179d72e80a762fec3d2ed2b2741ec9841a05f9c8bd3969ecf793c68d74f62d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ/bundle.json","state_url":"https://pith.science/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ/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":"2026-08-04T12:38:28Z","links":{"resolver":"https://pith.science/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ","bundle":"https://pith.science/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ/bundle.json","state":"https://pith.science/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F56FQMBKNQ2JQ7CWJUHRHGELFJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:F56FQMBKNQ2JQ7CWJUHRHGELFJ","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":"e690218e4e65e053967d357ce6675586f562f7812eadcc7a668d75b836cd8670","cross_cats_sorted":["cs.CV","cs.LG","q-bio.QM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-12-18T15:56:29Z","title_canon_sha256":"a91874d4a510ec1246a7e49ca50ae3dce4f1546c1b28ee06cae54a9e4e66131f"},"schema_version":"1.0","source":{"id":"1912.08679","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.08679","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"arxiv_version","alias_value":"1912.08679v1","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.08679","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"pith_short_12","alias_value":"F56FQMBKNQ2J","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"pith_short_16","alias_value":"F56FQMBKNQ2JQ7CW","created_at":"2026-07-05T00:27:06Z"},{"alias_kind":"pith_short_8","alias_value":"F56FQMBK","created_at":"2026-07-05T00:27:06Z"}],"graph_snapshots":[{"event_id":"sha256:84179d72e80a762fec3d2ed2b2741ec9841a05f9c8bd3969ecf793c68d74f62d","target":"graph","created_at":"2026-07-05T00:27:06Z","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/1912.08679/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung ","authors_text":"Gemma Piella, Ilaria Bonavita, Mario Ceresa, Miguel A. Gonz\\'alez Ballester, Vicent Ribas, Xavier Rafael-Palou","cross_cats":["cs.CV","cs.LG","q-bio.QM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-12-18T15:56:29Z","title":"Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.08679","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":"sha256:5f090e8d347842f879de2a8dce8744722c27148119bf74b973d368776b90f699","target":"record","created_at":"2026-07-05T00:27:06Z","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":"e690218e4e65e053967d357ce6675586f562f7812eadcc7a668d75b836cd8670","cross_cats_sorted":["cs.CV","cs.LG","q-bio.QM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-12-18T15:56:29Z","title_canon_sha256":"a91874d4a510ec1246a7e49ca50ae3dce4f1546c1b28ee06cae54a9e4e66131f"},"schema_version":"1.0","source":{"id":"1912.08679","kind":"arxiv","version":1}},"canonical_sha256":"2f7c58302a6c34987c564d0f13988b2a4d40e813acb28583d83a795128f8e8e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f7c58302a6c34987c564d0f13988b2a4d40e813acb28583d83a795128f8e8e5","first_computed_at":"2026-07-05T00:27:06.162822Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:27:06.162822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rSCNoOes7HQW583WqicEatLNxhMDMc9T0lcIZCPsg/FBBeeMgsmAHlvoHgFBq2WKdqPkQdwwGVQQgFOW33JuBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:27:06.163321Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.08679","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5f090e8d347842f879de2a8dce8744722c27148119bf74b973d368776b90f699","sha256:84179d72e80a762fec3d2ed2b2741ec9841a05f9c8bd3969ecf793c68d74f62d"],"state_sha256":"9f23cd2edf944562cd5449ee4a934c78ee98aca3cec5626baf6286a2db979201"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZGAf5SEUkkwkZ/HhaLNbGTSlRbjHAc9vq30BfyZKJZXl+9PZj4fLh0mAuDCZ0CU4deNltGGkp6/dsfMzkxlvBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:38:28.659903Z","bundle_sha256":"1c405fb2386f547a71b25b144566a1098646ef660503cf784aa05368f6eb516f"}}