{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:7FRP25W3ZADNVLE7FDFZH3QTXL","short_pith_number":"pith:7FRP25W3","canonical_record":{"source":{"id":"2203.07815","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-15T12:11:05Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1ec0c7f7b015e1d7908f1e5ac20aabfc62311d62e6211ad07336a5e8cfb0cc26","abstract_canon_sha256":"a49f37d4cb88dee5c67b6a3911d6268ff7c9bf9ab63f1d4871dc91e5f4729579"},"schema_version":"1.0"},"canonical_sha256":"f962fd76dbc806daac9f28cb93ee13bae96c9f7efcd3c2ce6973882f804a451d","source":{"kind":"arxiv","id":"2203.07815","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.07815","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"arxiv_version","alias_value":"2203.07815v2","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.07815","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"pith_short_12","alias_value":"7FRP25W3ZADN","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"pith_short_16","alias_value":"7FRP25W3ZADNVLE7","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"pith_short_8","alias_value":"7FRP25W3","created_at":"2026-07-05T05:02:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:7FRP25W3ZADNVLE7FDFZH3QTXL","target":"record","payload":{"canonical_record":{"source":{"id":"2203.07815","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-15T12:11:05Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1ec0c7f7b015e1d7908f1e5ac20aabfc62311d62e6211ad07336a5e8cfb0cc26","abstract_canon_sha256":"a49f37d4cb88dee5c67b6a3911d6268ff7c9bf9ab63f1d4871dc91e5f4729579"},"schema_version":"1.0"},"canonical_sha256":"f962fd76dbc806daac9f28cb93ee13bae96c9f7efcd3c2ce6973882f804a451d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:02:24.831286Z","signature_b64":"dX9sSAl1PwAlaX4BkdruwlXXIQbv7V5L1Rh3hLNWX7WBV3QuaY1EjUi9oHDP7OjMaK0dZU/sfm7D4+TPhme6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f962fd76dbc806daac9f28cb93ee13bae96c9f7efcd3c2ce6973882f804a451d","last_reissued_at":"2026-07-05T05:02:24.830797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:02:24.830797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.07815","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":"2026-07-05T05:02:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PWlM74xI5mhLr4T9Ts1ptr6m2z5WIjcTvkPwMCIhlyjTFGpKJqsnOQCc+CyE7vREF6EmwF1spcJITVaxqde+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T14:42:58.128892Z"},"content_sha256":"4e8b8f74e443f3d990bea536418dfb2e22e0efe6855a3db95e94558657df4cb1","schema_version":"1.0","event_id":"sha256:4e8b8f74e443f3d990bea536418dfb2e22e0efe6855a3db95e94558657df4cb1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:7FRP25W3ZADNVLE7FDFZH3QTXL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Chen Qin, Pedro Sanchez, Sotirios A. Tsaftaris, Tian Xia","submitted_at":"2022-03-15T12:11:05Z","abstract_excerpt":"Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with random transformations has been shown to help and became a ubiquitous technique for training neural networks. Here, we propose a novel adversarial counterfactual augmentation scheme that aims at finding the most \\textit{effective} synthesised images to improve downstream tasks, given a pre-trained generative model. Specifically, we construct an adversarial game where we update the input \\textit{conditional factor} of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.07815","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/2203.07815/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":"2026-07-05T05:02:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CTPg4Cb6PCsvT97ozn/XYx3QfCwREjSDKl3BXo4c/3WSUIhJBafgSeYGdqTxih+6p7pNNA5Yo3OHegUDTyrrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T14:42:58.129261Z"},"content_sha256":"a8ceff8375ad6e224b2161705385d1bb673954fffc1874dfdfd2d229af00e0e1","schema_version":"1.0","event_id":"sha256:a8ceff8375ad6e224b2161705385d1bb673954fffc1874dfdfd2d229af00e0e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7FRP25W3ZADNVLE7FDFZH3QTXL/bundle.json","state_url":"https://pith.science/pith/7FRP25W3ZADNVLE7FDFZH3QTXL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7FRP25W3ZADNVLE7FDFZH3QTXL/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":"2026-07-19T14:42:58Z","links":{"resolver":"https://pith.science/pith/7FRP25W3ZADNVLE7FDFZH3QTXL","bundle":"https://pith.science/pith/7FRP25W3ZADNVLE7FDFZH3QTXL/bundle.json","state":"https://pith.science/pith/7FRP25W3ZADNVLE7FDFZH3QTXL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7FRP25W3ZADNVLE7FDFZH3QTXL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7FRP25W3ZADNVLE7FDFZH3QTXL","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":"a49f37d4cb88dee5c67b6a3911d6268ff7c9bf9ab63f1d4871dc91e5f4729579","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-15T12:11:05Z","title_canon_sha256":"1ec0c7f7b015e1d7908f1e5ac20aabfc62311d62e6211ad07336a5e8cfb0cc26"},"schema_version":"1.0","source":{"id":"2203.07815","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.07815","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"arxiv_version","alias_value":"2203.07815v2","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.07815","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"pith_short_12","alias_value":"7FRP25W3ZADN","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"pith_short_16","alias_value":"7FRP25W3ZADNVLE7","created_at":"2026-07-05T05:02:24Z"},{"alias_kind":"pith_short_8","alias_value":"7FRP25W3","created_at":"2026-07-05T05:02:24Z"}],"graph_snapshots":[{"event_id":"sha256:a8ceff8375ad6e224b2161705385d1bb673954fffc1874dfdfd2d229af00e0e1","target":"graph","created_at":"2026-07-05T05:02:24Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.07815/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with random transformations has been shown to help and became a ubiquitous technique for training neural networks. Here, we propose a novel adversarial counterfactual augmentation scheme that aims at finding the most \\textit{effective} synthesised images to improve downstream tasks, given a pre-trained generative model. Specifically, we construct an adversarial game where we update the input \\textit{conditional factor} of","authors_text":"Chen Qin, Pedro Sanchez, Sotirios A. Tsaftaris, Tian Xia","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-15T12:11:05Z","title":"Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.07815","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":"sha256:4e8b8f74e443f3d990bea536418dfb2e22e0efe6855a3db95e94558657df4cb1","target":"record","created_at":"2026-07-05T05:02:24Z","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":"a49f37d4cb88dee5c67b6a3911d6268ff7c9bf9ab63f1d4871dc91e5f4729579","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-15T12:11:05Z","title_canon_sha256":"1ec0c7f7b015e1d7908f1e5ac20aabfc62311d62e6211ad07336a5e8cfb0cc26"},"schema_version":"1.0","source":{"id":"2203.07815","kind":"arxiv","version":2}},"canonical_sha256":"f962fd76dbc806daac9f28cb93ee13bae96c9f7efcd3c2ce6973882f804a451d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f962fd76dbc806daac9f28cb93ee13bae96c9f7efcd3c2ce6973882f804a451d","first_computed_at":"2026-07-05T05:02:24.830797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:02:24.830797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dX9sSAl1PwAlaX4BkdruwlXXIQbv7V5L1Rh3hLNWX7WBV3QuaY1EjUi9oHDP7OjMaK0dZU/sfm7D4+TPhme6AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:02:24.831286Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.07815","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e8b8f74e443f3d990bea536418dfb2e22e0efe6855a3db95e94558657df4cb1","sha256:a8ceff8375ad6e224b2161705385d1bb673954fffc1874dfdfd2d229af00e0e1"],"state_sha256":"3438ec47e49fdd139dd3e3ed1b84ec26e5f30ce70a63d8cb7e4e68a786d767da"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8lfQ8aPJtxiGfvXwDYHHMvqL7o4vTJWT6RYnTqJpRySVFsc+Suk68Mg4Isq+Q9Z3Q763ws8apzim18BPMxobAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-19T14:42:58.131374Z","bundle_sha256":"f0b0dfd4321510a42b15a3ea917baac1cd01c64d1ab0219c275457993295214e"}}