{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:HA7TBFGVKSOPMYZJW2VYT72CCR","short_pith_number":"pith:HA7TBFGV","canonical_record":{"source":{"id":"2210.16034","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-28T10:15:36Z","cross_cats_sorted":[],"title_canon_sha256":"436d8bcd42b1f7fdeb27b5994a0b7383c558cc48b6b8f4be50479fd2e6322fa0","abstract_canon_sha256":"ed87908603a0fedf26386401fd2e161b4ea8e8cd46685b35732716d170c21e13"},"schema_version":"1.0"},"canonical_sha256":"383f3094d5549cf66329b6ab89ff421475138b31157e43b2aa77a963f43a759a","source":{"kind":"arxiv","id":"2210.16034","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.16034","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"arxiv_version","alias_value":"2210.16034v1","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16034","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"pith_short_12","alias_value":"HA7TBFGVKSOP","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"pith_short_16","alias_value":"HA7TBFGVKSOPMYZJ","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"pith_short_8","alias_value":"HA7TBFGV","created_at":"2026-07-05T05:11:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:HA7TBFGVKSOPMYZJW2VYT72CCR","target":"record","payload":{"canonical_record":{"source":{"id":"2210.16034","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-28T10:15:36Z","cross_cats_sorted":[],"title_canon_sha256":"436d8bcd42b1f7fdeb27b5994a0b7383c558cc48b6b8f4be50479fd2e6322fa0","abstract_canon_sha256":"ed87908603a0fedf26386401fd2e161b4ea8e8cd46685b35732716d170c21e13"},"schema_version":"1.0"},"canonical_sha256":"383f3094d5549cf66329b6ab89ff421475138b31157e43b2aa77a963f43a759a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:29.862515Z","signature_b64":"sBk22vb2akoWFDARFBw94/LqQFm7yB13Tz2Ys9lBYuoKbob5ON14nI2meQa+LbIyZAzObY1CJytql4bAHuJ3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"383f3094d5549cf66329b6ab89ff421475138b31157e43b2aa77a963f43a759a","last_reissued_at":"2026-07-05T05:11:29.862163Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:29.862163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.16034","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":"2026-07-05T05:11:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HEESfYkCUo4oXFAC8pbRGUUmGHlehl7zELiPlf0HM1SJs43Mt8tXmm0wcPO3vkxaJ10xgIqhtdTYepjrukrKAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T07:44:57.118448Z"},"content_sha256":"f29eaf0a3bc491861904b4797018ab2bac68c691309b21453171209fc83266c1","schema_version":"1.0","event_id":"sha256:f29eaf0a3bc491861904b4797018ab2bac68c691309b21453171209fc83266c1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:HA7TBFGVKSOPMYZJW2VYT72CCR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Causal Representation Learning and Future Work for Medical Image Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changjie Lu","submitted_at":"2022-10-28T10:15:36Z","abstract_excerpt":"Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks. However, the out-of-distribution data and confounder, which have an unpredictable causal relationship, significantly degrade the performance of the existing models. Causal Representation Learning (CRL) has recently been a promising direction to address the causal relationship problem in vision understanding. This survey presents recent advances in CRL in vision. Firstly, we introduce the basic concept of causal inference. Secondly, we analyze the CRL theoret"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16034","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/2210.16034/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":"2026-07-05T05:11:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WTDCNEOHgZbgFWMN8cLPLdGJkEyon3hAxHFa3JRH4DQynSgx8HwjJKFYfBmpppmKpwQidZwUbd1Nz2MmBd63Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T07:44:57.118949Z"},"content_sha256":"59e3c9fed0bcb0f343feff339054f81b5470ae281c20cf5cea3ae743cc02468d","schema_version":"1.0","event_id":"sha256:59e3c9fed0bcb0f343feff339054f81b5470ae281c20cf5cea3ae743cc02468d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HA7TBFGVKSOPMYZJW2VYT72CCR/bundle.json","state_url":"https://pith.science/pith/HA7TBFGVKSOPMYZJW2VYT72CCR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HA7TBFGVKSOPMYZJW2VYT72CCR/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":"2026-08-01T07:44:57Z","links":{"resolver":"https://pith.science/pith/HA7TBFGVKSOPMYZJW2VYT72CCR","bundle":"https://pith.science/pith/HA7TBFGVKSOPMYZJW2VYT72CCR/bundle.json","state":"https://pith.science/pith/HA7TBFGVKSOPMYZJW2VYT72CCR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HA7TBFGVKSOPMYZJW2VYT72CCR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HA7TBFGVKSOPMYZJW2VYT72CCR","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":"ed87908603a0fedf26386401fd2e161b4ea8e8cd46685b35732716d170c21e13","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-28T10:15:36Z","title_canon_sha256":"436d8bcd42b1f7fdeb27b5994a0b7383c558cc48b6b8f4be50479fd2e6322fa0"},"schema_version":"1.0","source":{"id":"2210.16034","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.16034","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"arxiv_version","alias_value":"2210.16034v1","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16034","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"pith_short_12","alias_value":"HA7TBFGVKSOP","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"pith_short_16","alias_value":"HA7TBFGVKSOPMYZJ","created_at":"2026-07-05T05:11:29Z"},{"alias_kind":"pith_short_8","alias_value":"HA7TBFGV","created_at":"2026-07-05T05:11:29Z"}],"graph_snapshots":[{"event_id":"sha256:59e3c9fed0bcb0f343feff339054f81b5470ae281c20cf5cea3ae743cc02468d","target":"graph","created_at":"2026-07-05T05:11:29Z","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/2210.16034/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks. However, the out-of-distribution data and confounder, which have an unpredictable causal relationship, significantly degrade the performance of the existing models. Causal Representation Learning (CRL) has recently been a promising direction to address the causal relationship problem in vision understanding. This survey presents recent advances in CRL in vision. Firstly, we introduce the basic concept of causal inference. Secondly, we analyze the CRL theoret","authors_text":"Changjie Lu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-28T10:15:36Z","title":"A Survey on Causal Representation Learning and Future Work for Medical Image Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16034","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:f29eaf0a3bc491861904b4797018ab2bac68c691309b21453171209fc83266c1","target":"record","created_at":"2026-07-05T05:11:29Z","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":"ed87908603a0fedf26386401fd2e161b4ea8e8cd46685b35732716d170c21e13","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-28T10:15:36Z","title_canon_sha256":"436d8bcd42b1f7fdeb27b5994a0b7383c558cc48b6b8f4be50479fd2e6322fa0"},"schema_version":"1.0","source":{"id":"2210.16034","kind":"arxiv","version":1}},"canonical_sha256":"383f3094d5549cf66329b6ab89ff421475138b31157e43b2aa77a963f43a759a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"383f3094d5549cf66329b6ab89ff421475138b31157e43b2aa77a963f43a759a","first_computed_at":"2026-07-05T05:11:29.862163Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:11:29.862163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sBk22vb2akoWFDARFBw94/LqQFm7yB13Tz2Ys9lBYuoKbob5ON14nI2meQa+LbIyZAzObY1CJytql4bAHuJ3BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:11:29.862515Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.16034","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f29eaf0a3bc491861904b4797018ab2bac68c691309b21453171209fc83266c1","sha256:59e3c9fed0bcb0f343feff339054f81b5470ae281c20cf5cea3ae743cc02468d"],"state_sha256":"328374e57e2216d5cbe2d2cb1c2c88f1e713f1c421fdfb0a768f3db4c88cf993"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tI7LCTwnyDt1H7pTPApnrunD+rgG4f2aku/7aSbjF06i3Ewbhsnx9Gh/GJoOK3tQ2VrGL2IWnAZQKCrEAcDoCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T07:44:57.124333Z","bundle_sha256":"318ba8274a2eaf39b44d6f760bbd6281d41088316a60c7061515f0c7662d27d8"}}