{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ELTQGBTH3ZKWQP7ZHIO7TV77JH","short_pith_number":"pith:ELTQGBTH","canonical_record":{"source":{"id":"1904.13018","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-30T02:18:23Z","cross_cats_sorted":[],"title_canon_sha256":"05ba56867d19dfeffd200304bdcbd60de535f967720ef52ee2dffe382039a9ed","abstract_canon_sha256":"1b67e6fe03e137607df562d00dcdda1fbdab53680edf09a6128a2616f344eb2f"},"schema_version":"1.0"},"canonical_sha256":"22e7030667de55683ff93a1df9d7ff49c2ab88e78d289c6283dc0c5a96e08c1b","source":{"kind":"arxiv","id":"1904.13018","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.13018","created_at":"2026-05-17T23:47:24Z"},{"alias_kind":"arxiv_version","alias_value":"1904.13018v1","created_at":"2026-05-17T23:47:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.13018","created_at":"2026-05-17T23:47:24Z"},{"alias_kind":"pith_short_12","alias_value":"ELTQGBTH3ZKW","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"ELTQGBTH3ZKWQP7Z","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"ELTQGBTH","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ELTQGBTH3ZKWQP7ZHIO7TV77JH","target":"record","payload":{"canonical_record":{"source":{"id":"1904.13018","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-30T02:18:23Z","cross_cats_sorted":[],"title_canon_sha256":"05ba56867d19dfeffd200304bdcbd60de535f967720ef52ee2dffe382039a9ed","abstract_canon_sha256":"1b67e6fe03e137607df562d00dcdda1fbdab53680edf09a6128a2616f344eb2f"},"schema_version":"1.0"},"canonical_sha256":"22e7030667de55683ff93a1df9d7ff49c2ab88e78d289c6283dc0c5a96e08c1b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:24.187061Z","signature_b64":"+yejSZlrhUoHRqGA2y6s8jDygAZJNNFMWzldV+ZOpMkf0CwMuSkayKKk83pzw6rz5gTgMG9zUTiExEKdZZCwAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22e7030667de55683ff93a1df9d7ff49c2ab88e78d289c6283dc0c5a96e08c1b","last_reissued_at":"2026-05-17T23:47:24.186349Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:24.186349Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.13018","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":"2026-05-17T23:47:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uXdlv+tg9F47gsbEiSANR9Lbv7j/5RPn65FDwaZBzueJPpfnwXXxR38gg7Cndx4M2tDJU9/TG35reu4M256mBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T20:34:02.688295Z"},"content_sha256":"ad18040124869416b23214fcced4c914a9d57816dd7a457fbcc8b24dc5017061","schema_version":"1.0","event_id":"sha256:ad18040124869416b23214fcced4c914a9d57816dd7a457fbcc8b24dc5017061"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ELTQGBTH3ZKWQP7ZHIO7TV77JH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A self-attention based deep learning method for lesion attribute detection from CT reports","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ke Yan, Ronald M. Summers, Veit Sandfort, Yifan Peng, Zhiyong Lu","submitted_at":"2019-04-30T02:18:23Z","abstract_excerpt":"In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity. While these lesion attributes are rich and useful in many downstream clinical applications, how to extract them from the radiology reports is less studied. This paper outlines a novel deep learning method to automatically extract attributes of lesions of interest from the clinical text. Different from classical CNN models, we integrated the multi-head self-attention mechanism to handle the long-distance information i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.13018","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":"2026-05-17T23:47:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ppuXaKtpYjuyWBHX9EhckZ1oQdSy+VogZnfPjes2uVIdUPo/ueLBhjBk+9lgvlIRJEHwZZ4o1f1TN/6To4NwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T20:34:02.688663Z"},"content_sha256":"09bf58b011bec8389bff0587ab168c67897de53421b9cb65d122c56109296088","schema_version":"1.0","event_id":"sha256:09bf58b011bec8389bff0587ab168c67897de53421b9cb65d122c56109296088"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH/bundle.json","state_url":"https://pith.science/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH/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":"2026-06-02T20:34:02Z","links":{"resolver":"https://pith.science/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH","bundle":"https://pith.science/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH/bundle.json","state":"https://pith.science/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ELTQGBTH3ZKWQP7ZHIO7TV77JH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ELTQGBTH3ZKWQP7ZHIO7TV77JH","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":"1b67e6fe03e137607df562d00dcdda1fbdab53680edf09a6128a2616f344eb2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-30T02:18:23Z","title_canon_sha256":"05ba56867d19dfeffd200304bdcbd60de535f967720ef52ee2dffe382039a9ed"},"schema_version":"1.0","source":{"id":"1904.13018","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.13018","created_at":"2026-05-17T23:47:24Z"},{"alias_kind":"arxiv_version","alias_value":"1904.13018v1","created_at":"2026-05-17T23:47:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.13018","created_at":"2026-05-17T23:47:24Z"},{"alias_kind":"pith_short_12","alias_value":"ELTQGBTH3ZKW","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"ELTQGBTH3ZKWQP7Z","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"ELTQGBTH","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:09bf58b011bec8389bff0587ab168c67897de53421b9cb65d122c56109296088","target":"graph","created_at":"2026-05-17T23:47:24Z","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":"In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity. While these lesion attributes are rich and useful in many downstream clinical applications, how to extract them from the radiology reports is less studied. This paper outlines a novel deep learning method to automatically extract attributes of lesions of interest from the clinical text. Different from classical CNN models, we integrated the multi-head self-attention mechanism to handle the long-distance information i","authors_text":"Ke Yan, Ronald M. Summers, Veit Sandfort, Yifan Peng, Zhiyong Lu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-30T02:18:23Z","title":"A self-attention based deep learning method for lesion attribute detection from CT reports"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.13018","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:ad18040124869416b23214fcced4c914a9d57816dd7a457fbcc8b24dc5017061","target":"record","created_at":"2026-05-17T23:47:24Z","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":"1b67e6fe03e137607df562d00dcdda1fbdab53680edf09a6128a2616f344eb2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-30T02:18:23Z","title_canon_sha256":"05ba56867d19dfeffd200304bdcbd60de535f967720ef52ee2dffe382039a9ed"},"schema_version":"1.0","source":{"id":"1904.13018","kind":"arxiv","version":1}},"canonical_sha256":"22e7030667de55683ff93a1df9d7ff49c2ab88e78d289c6283dc0c5a96e08c1b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22e7030667de55683ff93a1df9d7ff49c2ab88e78d289c6283dc0c5a96e08c1b","first_computed_at":"2026-05-17T23:47:24.186349Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:24.186349Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+yejSZlrhUoHRqGA2y6s8jDygAZJNNFMWzldV+ZOpMkf0CwMuSkayKKk83pzw6rz5gTgMG9zUTiExEKdZZCwAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:24.187061Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.13018","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad18040124869416b23214fcced4c914a9d57816dd7a457fbcc8b24dc5017061","sha256:09bf58b011bec8389bff0587ab168c67897de53421b9cb65d122c56109296088"],"state_sha256":"5896a17abbc59dfcdf01b8f3da7aa1d1cc05e3ce158a1462b014f59792718963"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T9WeehJpGfxm92Z37ni4WzW/HIeHl5bKXNZIU/xkxNDfK/woPSdrLVL/DU+Yj70U0mLsgZWFMksXQql1YX6UAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T20:34:02.690709Z","bundle_sha256":"083ae1e136a1756b50bedf0003d44cc4844997b561e6cf69df37b005b311a426"}}