{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:AZW7DJIVHLRUZQYFVZ7DPFS2VJ","short_pith_number":"pith:AZW7DJIV","canonical_record":{"source":{"id":"2002.06588","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-16T15:04:52Z","cross_cats_sorted":[],"title_canon_sha256":"fef522e7ff5d060ddf73872829f81f759f3fe6aa51236d8d1bf4367d98a969c9","abstract_canon_sha256":"84053ccb6f110426b8a9e68ce2f669e720538f79bb81d8d9311f86227775fd1d"},"schema_version":"1.0"},"canonical_sha256":"066df1a5153ae34cc305ae7e37965aaa4369c4e9a18866ea331b84172c883f61","source":{"kind":"arxiv","id":"2002.06588","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.06588","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"arxiv_version","alias_value":"2002.06588v1","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06588","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_12","alias_value":"AZW7DJIVHLRU","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_16","alias_value":"AZW7DJIVHLRUZQYF","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_8","alias_value":"AZW7DJIV","created_at":"2026-07-05T00:41:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:AZW7DJIVHLRUZQYFVZ7DPFS2VJ","target":"record","payload":{"canonical_record":{"source":{"id":"2002.06588","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-16T15:04:52Z","cross_cats_sorted":[],"title_canon_sha256":"fef522e7ff5d060ddf73872829f81f759f3fe6aa51236d8d1bf4367d98a969c9","abstract_canon_sha256":"84053ccb6f110426b8a9e68ce2f669e720538f79bb81d8d9311f86227775fd1d"},"schema_version":"1.0"},"canonical_sha256":"066df1a5153ae34cc305ae7e37965aaa4369c4e9a18866ea331b84172c883f61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:07.502814Z","signature_b64":"IQvz1a9e2n8gHbNNFDAIlfoBREEqs3oBFWEu8cOZyAZNDl+6Y9rg/rDbTmHdiwdCUZ1vOYeXAtk6dfDWWPYfCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"066df1a5153ae34cc305ae7e37965aaa4369c4e9a18866ea331b84172c883f61","last_reissued_at":"2026-07-05T00:41:07.502393Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:07.502393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.06588","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":"2026-07-05T00:41:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FKdZBGi4BJqND5qb+LEoqIbdLs2Vaz0/BkMxRRKJPCtL++ns4LOtinKUmhg8fPPtXXC546yGiABW7Zv00EYeAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T22:35:46.422229Z"},"content_sha256":"2032e38054153a7f4140c54cf0f2999d0358e63d7a6b6eec7387fa76e5b201a9","schema_version":"1.0","event_id":"sha256:2032e38054153a7f4140c54cf0f2999d0358e63d7a6b6eec7387fa76e5b201a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:AZW7DJIVHLRUZQYFVZ7DPFS2VJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aisha Al Busaidi, Antanas Montvila, David A. Wood, Emily Guilhem, Gareth Barker, James H. Cole, Jeremy Lynch, Juveria Siddiqui, Keena Patel, Martin Kiik, Matthew Townend, Naveen Gadapa, Sebastian Ourselin, Sina Kafiabadi, Thomas C. Booth, Thomas Varsavsky","submitted_at":"2020-02-16T15:04:52Z","abstract_excerpt":"Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06588","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/2002.06588/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":"2026-07-05T00:41:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ks/HC/9tZAL0UuylGr164Ya/N12BHOfzH3Pnd8NWwjKMupm/J5dAvTfu8AcvVXhL8SkAX7qknklR7Jo4urvJAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T22:35:46.422771Z"},"content_sha256":"a6c7b917db3b862b0260b4663cff3428f2c2249cd728b1c58e1a6b9f2b1e46ce","schema_version":"1.0","event_id":"sha256:a6c7b917db3b862b0260b4663cff3428f2c2249cd728b1c58e1a6b9f2b1e46ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ/bundle.json","state_url":"https://pith.science/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ/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":"2026-08-03T22:35:46Z","links":{"resolver":"https://pith.science/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ","bundle":"https://pith.science/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ/bundle.json","state":"https://pith.science/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AZW7DJIVHLRUZQYFVZ7DPFS2VJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:AZW7DJIVHLRUZQYFVZ7DPFS2VJ","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":"84053ccb6f110426b8a9e68ce2f669e720538f79bb81d8d9311f86227775fd1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-16T15:04:52Z","title_canon_sha256":"fef522e7ff5d060ddf73872829f81f759f3fe6aa51236d8d1bf4367d98a969c9"},"schema_version":"1.0","source":{"id":"2002.06588","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.06588","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"arxiv_version","alias_value":"2002.06588v1","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06588","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_12","alias_value":"AZW7DJIVHLRU","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_16","alias_value":"AZW7DJIVHLRUZQYF","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_8","alias_value":"AZW7DJIV","created_at":"2026-07-05T00:41:07Z"}],"graph_snapshots":[{"event_id":"sha256:a6c7b917db3b862b0260b4663cff3428f2c2249cd728b1c58e1a6b9f2b1e46ce","target":"graph","created_at":"2026-07-05T00:41:07Z","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/2002.06588/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label the","authors_text":"Aisha Al Busaidi, Antanas Montvila, David A. Wood, Emily Guilhem, Gareth Barker, James H. Cole, Jeremy Lynch, Juveria Siddiqui, Keena Patel, Martin Kiik, Matthew Townend, Naveen Gadapa, Sebastian Ourselin, Sina Kafiabadi, Thomas C. Booth, Thomas Varsavsky","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-16T15:04:52Z","title":"Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06588","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":"sha256:2032e38054153a7f4140c54cf0f2999d0358e63d7a6b6eec7387fa76e5b201a9","target":"record","created_at":"2026-07-05T00:41:07Z","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":"84053ccb6f110426b8a9e68ce2f669e720538f79bb81d8d9311f86227775fd1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-16T15:04:52Z","title_canon_sha256":"fef522e7ff5d060ddf73872829f81f759f3fe6aa51236d8d1bf4367d98a969c9"},"schema_version":"1.0","source":{"id":"2002.06588","kind":"arxiv","version":1}},"canonical_sha256":"066df1a5153ae34cc305ae7e37965aaa4369c4e9a18866ea331b84172c883f61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"066df1a5153ae34cc305ae7e37965aaa4369c4e9a18866ea331b84172c883f61","first_computed_at":"2026-07-05T00:41:07.502393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:41:07.502393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IQvz1a9e2n8gHbNNFDAIlfoBREEqs3oBFWEu8cOZyAZNDl+6Y9rg/rDbTmHdiwdCUZ1vOYeXAtk6dfDWWPYfCA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:41:07.502814Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.06588","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2032e38054153a7f4140c54cf0f2999d0358e63d7a6b6eec7387fa76e5b201a9","sha256:a6c7b917db3b862b0260b4663cff3428f2c2249cd728b1c58e1a6b9f2b1e46ce"],"state_sha256":"cc99fa8cfcb2f26febd4983cbc64a47b1d52917ff06d9d436778417527ee9082"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X0km/DJS1V1HlFpcMu8UHVCpfyrmVbTPHrN0+k/C0ZT21UTiAmovE0/hnlNZwmAJbwoCqEINiOoUfGjqT+aVCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T22:35:46.428245Z","bundle_sha256":"2e7a2cfa0335dbb5d62efb13718a3d2ff217a8c0b1d0408a00ce37fd6043f6df"}}