{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:V2AKE6KYC6JE4LXLJBQ535PLX2","short_pith_number":"pith:V2AKE6KY","canonical_record":{"source":{"id":"2206.05498","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-11T11:04:13Z","cross_cats_sorted":["cs.AI","cs.GL"],"title_canon_sha256":"6693491ed2807b8b9f8a8afad46c23f30cdf7ed226b173b63879ab67194bd57b","abstract_canon_sha256":"933b97bdc3b2541068e7ec20a69c906e239fa053a205391d4ced63c062ee1b25"},"schema_version":"1.0"},"canonical_sha256":"ae80a2795817924e2eeb4861ddf5ebbe8ca420d1b1a0e0ea9ebc28edc9820fab","source":{"kind":"arxiv","id":"2206.05498","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.05498","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"arxiv_version","alias_value":"2206.05498v2","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.05498","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"pith_short_12","alias_value":"V2AKE6KYC6JE","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"pith_short_16","alias_value":"V2AKE6KYC6JE4LXL","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"pith_short_8","alias_value":"V2AKE6KY","created_at":"2026-07-05T05:19:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:V2AKE6KYC6JE4LXLJBQ535PLX2","target":"record","payload":{"canonical_record":{"source":{"id":"2206.05498","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-11T11:04:13Z","cross_cats_sorted":["cs.AI","cs.GL"],"title_canon_sha256":"6693491ed2807b8b9f8a8afad46c23f30cdf7ed226b173b63879ab67194bd57b","abstract_canon_sha256":"933b97bdc3b2541068e7ec20a69c906e239fa053a205391d4ced63c062ee1b25"},"schema_version":"1.0"},"canonical_sha256":"ae80a2795817924e2eeb4861ddf5ebbe8ca420d1b1a0e0ea9ebc28edc9820fab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:35.352350Z","signature_b64":"Y79g5i5ODi9OCeWK00Pcsdj7oTFPRvu1ppdbDX5JZlsg3yBiQMQe3bE+TVXF1Kaz/6AfJ2oxTCsBPCfeb0XdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae80a2795817924e2eeb4861ddf5ebbe8ca420d1b1a0e0ea9ebc28edc9820fab","last_reissued_at":"2026-07-05T05:19:35.351855Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:35.351855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.05498","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":"2026-07-05T05:19:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qDZp+wkSX4PjOeO4EPsLZpa0J6PKDV7+rqwTqBo55OHdcgZ26e17rZZCcZd1E3UIdvaeJtcVawCuWW2TEDtTCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T05:43:37.304495Z"},"content_sha256":"3abad8698140fd8aefb4379b9cda9f5077e8fa5561b8397dc6018b984ad6547f","schema_version":"1.0","event_id":"sha256:3abad8698140fd8aefb4379b9cda9f5077e8fa5561b8397dc6018b984ad6547f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:V2AKE6KYC6JE4LXLJBQ535PLX2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Review of Causality for Learning Algorithms in Medical Image Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.GL"],"primary_cat":"cs.CV","authors_text":"Athanasios Vlontzos, Bernhard Kainz, Daniel Rueckert","submitted_at":"2022-06-11T11:04:13Z","abstract_excerpt":"Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.05498","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.05498/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":"2026-07-05T05:19:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TROXtoFdAjwzs51/91N7C2FrIBYPteVbJeliZvoaxIe6MRomjZZIKIxJzvn6sX9J7NpKJc8l/Rz+PHzKMNkpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T05:43:37.305025Z"},"content_sha256":"7cb263f8dca6ee5045b501b942ccb20d28a9164175c70a9fe801fa1846703122","schema_version":"1.0","event_id":"sha256:7cb263f8dca6ee5045b501b942ccb20d28a9164175c70a9fe801fa1846703122"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V2AKE6KYC6JE4LXLJBQ535PLX2/bundle.json","state_url":"https://pith.science/pith/V2AKE6KYC6JE4LXLJBQ535PLX2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V2AKE6KYC6JE4LXLJBQ535PLX2/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":"2026-08-04T05:43:37Z","links":{"resolver":"https://pith.science/pith/V2AKE6KYC6JE4LXLJBQ535PLX2","bundle":"https://pith.science/pith/V2AKE6KYC6JE4LXLJBQ535PLX2/bundle.json","state":"https://pith.science/pith/V2AKE6KYC6JE4LXLJBQ535PLX2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V2AKE6KYC6JE4LXLJBQ535PLX2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:V2AKE6KYC6JE4LXLJBQ535PLX2","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":"933b97bdc3b2541068e7ec20a69c906e239fa053a205391d4ced63c062ee1b25","cross_cats_sorted":["cs.AI","cs.GL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-11T11:04:13Z","title_canon_sha256":"6693491ed2807b8b9f8a8afad46c23f30cdf7ed226b173b63879ab67194bd57b"},"schema_version":"1.0","source":{"id":"2206.05498","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.05498","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"arxiv_version","alias_value":"2206.05498v2","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.05498","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"pith_short_12","alias_value":"V2AKE6KYC6JE","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"pith_short_16","alias_value":"V2AKE6KYC6JE4LXL","created_at":"2026-07-05T05:19:35Z"},{"alias_kind":"pith_short_8","alias_value":"V2AKE6KY","created_at":"2026-07-05T05:19:35Z"}],"graph_snapshots":[{"event_id":"sha256:7cb263f8dca6ee5045b501b942ccb20d28a9164175c70a9fe801fa1846703122","target":"graph","created_at":"2026-07-05T05:19:35Z","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/2206.05498/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust","authors_text":"Athanasios Vlontzos, Bernhard Kainz, Daniel Rueckert","cross_cats":["cs.AI","cs.GL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-11T11:04:13Z","title":"A Review of Causality for Learning Algorithms in Medical Image Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.05498","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":"sha256:3abad8698140fd8aefb4379b9cda9f5077e8fa5561b8397dc6018b984ad6547f","target":"record","created_at":"2026-07-05T05:19:35Z","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":"933b97bdc3b2541068e7ec20a69c906e239fa053a205391d4ced63c062ee1b25","cross_cats_sorted":["cs.AI","cs.GL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-11T11:04:13Z","title_canon_sha256":"6693491ed2807b8b9f8a8afad46c23f30cdf7ed226b173b63879ab67194bd57b"},"schema_version":"1.0","source":{"id":"2206.05498","kind":"arxiv","version":2}},"canonical_sha256":"ae80a2795817924e2eeb4861ddf5ebbe8ca420d1b1a0e0ea9ebc28edc9820fab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae80a2795817924e2eeb4861ddf5ebbe8ca420d1b1a0e0ea9ebc28edc9820fab","first_computed_at":"2026-07-05T05:19:35.351855Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:19:35.351855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y79g5i5ODi9OCeWK00Pcsdj7oTFPRvu1ppdbDX5JZlsg3yBiQMQe3bE+TVXF1Kaz/6AfJ2oxTCsBPCfeb0XdBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:19:35.352350Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.05498","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3abad8698140fd8aefb4379b9cda9f5077e8fa5561b8397dc6018b984ad6547f","sha256:7cb263f8dca6ee5045b501b942ccb20d28a9164175c70a9fe801fa1846703122"],"state_sha256":"5cfd17046bfe287d1e9f30fa42c44dda394298a0659aa8b93abbd413a04f3a8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7AIIv1X5hcDirjGM3A4P6ybavplEAiOzEeY2H8AdwQ0MEwYJaopAJOBtKkUDmtUtVP2Hy5FYIv/DxaPtGCtfAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T05:43:37.309025Z","bundle_sha256":"23e9855717f71fe7310685b26168053713f6bce4f5f5d9e6fb054d80bb066c57"}}