{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:6N3NUMHXEHXWFF5HMFTZZARAON","short_pith_number":"pith:6N3NUMHX","canonical_record":{"source":{"id":"2207.04343","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-09T22:09:37Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"93edc1a8a3e77b70d451e25585d549cb20f45780b8bfde00b75f0b26ae7d0859","abstract_canon_sha256":"ee02693dc137599fe202adb70abe3c7a3075f45a050271827c3ea266828be25a"},"schema_version":"1.0"},"canonical_sha256":"f376da30f721ef6297a761679c822073658382f90a4057b0133e72b92075e6af","source":{"kind":"arxiv","id":"2207.04343","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04343","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04343v1","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04343","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"pith_short_12","alias_value":"6N3NUMHXEHXW","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"pith_short_16","alias_value":"6N3NUMHXEHXWFF5H","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"pith_short_8","alias_value":"6N3NUMHX","created_at":"2026-07-05T04:38:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:6N3NUMHXEHXWFF5HMFTZZARAON","target":"record","payload":{"canonical_record":{"source":{"id":"2207.04343","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-09T22:09:37Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"93edc1a8a3e77b70d451e25585d549cb20f45780b8bfde00b75f0b26ae7d0859","abstract_canon_sha256":"ee02693dc137599fe202adb70abe3c7a3075f45a050271827c3ea266828be25a"},"schema_version":"1.0"},"canonical_sha256":"f376da30f721ef6297a761679c822073658382f90a4057b0133e72b92075e6af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:38:58.287772Z","signature_b64":"4xJd8LCPczlQFFc/C+NwI9shMOY+yzOExGvXGDdCv6pJImvCPKSUSKoqq/nKciaykWmti3boYR0J/Qsd6EYZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f376da30f721ef6297a761679c822073658382f90a4057b0133e72b92075e6af","last_reissued_at":"2026-07-05T04:38:58.287310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:38:58.287310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.04343","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":"2026-07-05T04:38:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kym5Sg8iYNdwKPzWB1/4+7MwZWIEy3bGh5ePeChK+sh0XsYRd8kY0YSAHyqbzSVv9BIU/nJTxK5COHpWCaO2BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T22:26:30.183262Z"},"content_sha256":"b4d919d4910e5b2f2d276eab3b76df036b9d9564b7c24d4d46a115be14d67be4","schema_version":"1.0","event_id":"sha256:b4d919d4910e5b2f2d276eab3b76df036b9d9564b7c24d4d46a115be14d67be4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:6N3NUMHXEHXWFF5HMFTZZARAON","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explaining Chest X-ray Pathologies in Natural Language","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Bartlomiej Papiez, Cornelius Emde, Guy Parsons, Maxime Kayser, Oana-Maria Camburu, Thomas Lukasiewicz","submitted_at":"2022-07-09T22:09:37Z","abstract_excerpt":"Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the task of generating natural language explanations (NLEs) to justify predictions made on medical images. NLEs are human-friendly and comprehensive, and enable the training of intrinsically explainable models. To this goal, we introduce MIMIC-NLE, the first, large-scale, medical imagi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.04343","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/2207.04343/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":"2026-07-05T04:38:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y/HDY+diJhxo7fqeo8Ulz4M9loUoNt4lP7VTJJDCegJKVLWklQJ0zNti2BXrIzP5lvWf8c8vqiBomvijUcODDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T22:26:30.184261Z"},"content_sha256":"8865ad7b5da0a286823570bf0b522adfb0bc530a0087409862d9b9075f7f3e59","schema_version":"1.0","event_id":"sha256:8865ad7b5da0a286823570bf0b522adfb0bc530a0087409862d9b9075f7f3e59"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6N3NUMHXEHXWFF5HMFTZZARAON/bundle.json","state_url":"https://pith.science/pith/6N3NUMHXEHXWFF5HMFTZZARAON/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6N3NUMHXEHXWFF5HMFTZZARAON/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":"2026-07-23T22:26:30Z","links":{"resolver":"https://pith.science/pith/6N3NUMHXEHXWFF5HMFTZZARAON","bundle":"https://pith.science/pith/6N3NUMHXEHXWFF5HMFTZZARAON/bundle.json","state":"https://pith.science/pith/6N3NUMHXEHXWFF5HMFTZZARAON/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6N3NUMHXEHXWFF5HMFTZZARAON/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6N3NUMHXEHXWFF5HMFTZZARAON","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":"ee02693dc137599fe202adb70abe3c7a3075f45a050271827c3ea266828be25a","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-09T22:09:37Z","title_canon_sha256":"93edc1a8a3e77b70d451e25585d549cb20f45780b8bfde00b75f0b26ae7d0859"},"schema_version":"1.0","source":{"id":"2207.04343","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04343","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04343v1","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04343","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"pith_short_12","alias_value":"6N3NUMHXEHXW","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"pith_short_16","alias_value":"6N3NUMHXEHXWFF5H","created_at":"2026-07-05T04:38:58Z"},{"alias_kind":"pith_short_8","alias_value":"6N3NUMHX","created_at":"2026-07-05T04:38:58Z"}],"graph_snapshots":[{"event_id":"sha256:8865ad7b5da0a286823570bf0b522adfb0bc530a0087409862d9b9075f7f3e59","target":"graph","created_at":"2026-07-05T04:38:58Z","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/2207.04343/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the task of generating natural language explanations (NLEs) to justify predictions made on medical images. NLEs are human-friendly and comprehensive, and enable the training of intrinsically explainable models. To this goal, we introduce MIMIC-NLE, the first, large-scale, medical imagi","authors_text":"Bartlomiej Papiez, Cornelius Emde, Guy Parsons, Maxime Kayser, Oana-Maria Camburu, Thomas Lukasiewicz","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-09T22:09:37Z","title":"Explaining Chest X-ray Pathologies in Natural Language"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.04343","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:b4d919d4910e5b2f2d276eab3b76df036b9d9564b7c24d4d46a115be14d67be4","target":"record","created_at":"2026-07-05T04:38:58Z","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":"ee02693dc137599fe202adb70abe3c7a3075f45a050271827c3ea266828be25a","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-09T22:09:37Z","title_canon_sha256":"93edc1a8a3e77b70d451e25585d549cb20f45780b8bfde00b75f0b26ae7d0859"},"schema_version":"1.0","source":{"id":"2207.04343","kind":"arxiv","version":1}},"canonical_sha256":"f376da30f721ef6297a761679c822073658382f90a4057b0133e72b92075e6af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f376da30f721ef6297a761679c822073658382f90a4057b0133e72b92075e6af","first_computed_at":"2026-07-05T04:38:58.287310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:38:58.287310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4xJd8LCPczlQFFc/C+NwI9shMOY+yzOExGvXGDdCv6pJImvCPKSUSKoqq/nKciaykWmti3boYR0J/Qsd6EYZCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:38:58.287772Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.04343","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b4d919d4910e5b2f2d276eab3b76df036b9d9564b7c24d4d46a115be14d67be4","sha256:8865ad7b5da0a286823570bf0b522adfb0bc530a0087409862d9b9075f7f3e59"],"state_sha256":"59990367847d869dd48b91d5b045ceb201ee84b08ed5898bf9d1c402a082e311"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JSMVEnNpIxfOuNHFAaDAS67q+O2suK5NGsCb2k80nKcXvAz+atflAafFY1sgjABNjWyjp+I1Xmhn7IUQ7va8DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-23T22:26:30.186922Z","bundle_sha256":"af868d3912db3d34dba3517e260bfee761ca32bf171ae128afa9ceb6fa9a3ebb"}}