{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:V4I7FVG4DVWJQKVQED5WGKRVBI","short_pith_number":"pith:V4I7FVG4","canonical_record":{"source":{"id":"2211.13352","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-11-23T23:53:03Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"ad0d4462da6399e593c4e018a0339b4cff31cf949710c5d402de06f12f2bbc58","abstract_canon_sha256":"9e0628b8964ba374a8292de29395bc3f2678c87023d0889f3d3cf953e0acbc0b"},"schema_version":"1.0"},"canonical_sha256":"af11f2d4dc1d6c982ab020fb632a350a286d2ae05638571ccb24f9a143069b31","source":{"kind":"arxiv","id":"2211.13352","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.13352","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"arxiv_version","alias_value":"2211.13352v1","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13352","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"pith_short_12","alias_value":"V4I7FVG4DVWJ","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"pith_short_16","alias_value":"V4I7FVG4DVWJQKVQ","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"pith_short_8","alias_value":"V4I7FVG4","created_at":"2026-07-05T05:19:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:V4I7FVG4DVWJQKVQED5WGKRVBI","target":"record","payload":{"canonical_record":{"source":{"id":"2211.13352","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-11-23T23:53:03Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"ad0d4462da6399e593c4e018a0339b4cff31cf949710c5d402de06f12f2bbc58","abstract_canon_sha256":"9e0628b8964ba374a8292de29395bc3f2678c87023d0889f3d3cf953e0acbc0b"},"schema_version":"1.0"},"canonical_sha256":"af11f2d4dc1d6c982ab020fb632a350a286d2ae05638571ccb24f9a143069b31","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:02.681291Z","signature_b64":"1xNS+vUFVJcRaeUj76bxGRB4m4SX0DYcCtFYNLtGlhFHy8YESknmveKfPcTX/doDfCUmOFsxQmu68Dn+goXTBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af11f2d4dc1d6c982ab020fb632a350a286d2ae05638571ccb24f9a143069b31","last_reissued_at":"2026-07-05T05:19:02.680770Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:02.680770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.13352","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":"2026-07-05T05:19:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PAg7Nq7TUwzBlI2c7Ee5Mqpmm6QH4aOzBpK7M3VNVPUpdSrmeZ/kkRPC960qFY2vWqwb7/MRqmYHN0ruouktCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T12:38:40.504423Z"},"content_sha256":"c0ea274e832b334b2a2db6a31460bbe495177991d3fd9faeaa5640fd95454b76","schema_version":"1.0","event_id":"sha256:c0ea274e832b334b2a2db6a31460bbe495177991d3fd9faeaa5640fd95454b76"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:V4I7FVG4DVWJQKVQED5WGKRVBI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving dermatology classifiers across populations using images generated by large diffusion models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Adewole S. Adamson, Arjun K. Manrai, James A. Diao, Luke W. Sagers, Matthew Groh, Pranav Rajpurkar","submitted_at":"2022-11-23T23:53:03Z","abstract_excerpt":"Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annotation offer the best means for improving representation, new computational approaches for generating training data may also aid in mitigating the effects of sampling bias. In this paper, we show that DALL$\\cdot$E 2, a large-scale text-to-image diffusion model, can produce photorealistic images of skin disease across skin types. Using the Fitzpatrick 17k dataset as a benchmark, we demonstrate that augmenting training d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13352","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/2211.13352/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":"2026-07-05T05:19:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N1RAozI2UcPgj7iNbdI9avs9mFuw6JSYMm/uaSnzsclVprIxKSknL8Ab+pGenadpTA/7kDO6tTJyBF0SIaxTCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T12:38:40.508645Z"},"content_sha256":"ec7498d892597dba02219302205cb37c264c9e781f17ed3eee57945d96b1903b","schema_version":"1.0","event_id":"sha256:ec7498d892597dba02219302205cb37c264c9e781f17ed3eee57945d96b1903b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V4I7FVG4DVWJQKVQED5WGKRVBI/bundle.json","state_url":"https://pith.science/pith/V4I7FVG4DVWJQKVQED5WGKRVBI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V4I7FVG4DVWJQKVQED5WGKRVBI/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":"2026-08-13T12:38:40Z","links":{"resolver":"https://pith.science/pith/V4I7FVG4DVWJQKVQED5WGKRVBI","bundle":"https://pith.science/pith/V4I7FVG4DVWJQKVQED5WGKRVBI/bundle.json","state":"https://pith.science/pith/V4I7FVG4DVWJQKVQED5WGKRVBI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V4I7FVG4DVWJQKVQED5WGKRVBI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:V4I7FVG4DVWJQKVQED5WGKRVBI","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":"9e0628b8964ba374a8292de29395bc3f2678c87023d0889f3d3cf953e0acbc0b","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-11-23T23:53:03Z","title_canon_sha256":"ad0d4462da6399e593c4e018a0339b4cff31cf949710c5d402de06f12f2bbc58"},"schema_version":"1.0","source":{"id":"2211.13352","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.13352","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"arxiv_version","alias_value":"2211.13352v1","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13352","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"pith_short_12","alias_value":"V4I7FVG4DVWJ","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"pith_short_16","alias_value":"V4I7FVG4DVWJQKVQ","created_at":"2026-07-05T05:19:02Z"},{"alias_kind":"pith_short_8","alias_value":"V4I7FVG4","created_at":"2026-07-05T05:19:02Z"}],"graph_snapshots":[{"event_id":"sha256:ec7498d892597dba02219302205cb37c264c9e781f17ed3eee57945d96b1903b","target":"graph","created_at":"2026-07-05T05:19:02Z","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/2211.13352/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annotation offer the best means for improving representation, new computational approaches for generating training data may also aid in mitigating the effects of sampling bias. In this paper, we show that DALL$\\cdot$E 2, a large-scale text-to-image diffusion model, can produce photorealistic images of skin disease across skin types. Using the Fitzpatrick 17k dataset as a benchmark, we demonstrate that augmenting training d","authors_text":"Adewole S. Adamson, Arjun K. Manrai, James A. Diao, Luke W. Sagers, Matthew Groh, Pranav Rajpurkar","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-11-23T23:53:03Z","title":"Improving dermatology classifiers across populations using images generated by large diffusion models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13352","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:c0ea274e832b334b2a2db6a31460bbe495177991d3fd9faeaa5640fd95454b76","target":"record","created_at":"2026-07-05T05:19:02Z","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":"9e0628b8964ba374a8292de29395bc3f2678c87023d0889f3d3cf953e0acbc0b","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-11-23T23:53:03Z","title_canon_sha256":"ad0d4462da6399e593c4e018a0339b4cff31cf949710c5d402de06f12f2bbc58"},"schema_version":"1.0","source":{"id":"2211.13352","kind":"arxiv","version":1}},"canonical_sha256":"af11f2d4dc1d6c982ab020fb632a350a286d2ae05638571ccb24f9a143069b31","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af11f2d4dc1d6c982ab020fb632a350a286d2ae05638571ccb24f9a143069b31","first_computed_at":"2026-07-05T05:19:02.680770Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:19:02.680770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1xNS+vUFVJcRaeUj76bxGRB4m4SX0DYcCtFYNLtGlhFHy8YESknmveKfPcTX/doDfCUmOFsxQmu68Dn+goXTBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:19:02.681291Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.13352","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0ea274e832b334b2a2db6a31460bbe495177991d3fd9faeaa5640fd95454b76","sha256:ec7498d892597dba02219302205cb37c264c9e781f17ed3eee57945d96b1903b"],"state_sha256":"1582a846777e34a8d9b583827978ba17c4078ed9cd046699aebc75f34edb5525"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g5gV+JqsgMIYNYKYHkpiMfAbcpRqqRA3tQhlGicVsINGqcftgeXcwFscA7LwnsLOlelZoDs4Ke3ASiXwF19XAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T12:38:40.533125Z","bundle_sha256":"b4d7f5798d1cac8031d6a4b306a33a3885fdeb19bc80b048d738fa43dc6af2a8"}}