{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:S7T6SKUPIZNJD4QBESFYCXKSMX","short_pith_number":"pith:S7T6SKUP","canonical_record":{"source":{"id":"1804.04338","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-12T06:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"3f9fe797a715fbc7e0c15a2d6995a4b44c145883f3568eec447bfb633d838e57","abstract_canon_sha256":"1a3551380a61438ecde49c654af54f7d0bc23676720081cb057fd0834d05d984"},"schema_version":"1.0"},"canonical_sha256":"97e7e92a8f465a91f201248b815d5265de2ec5e2a7dc99afb78ffa37fe6ae2b8","source":{"kind":"arxiv","id":"1804.04338","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.04338","created_at":"2026-05-18T00:18:39Z"},{"alias_kind":"arxiv_version","alias_value":"1804.04338v1","created_at":"2026-05-18T00:18:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.04338","created_at":"2026-05-18T00:18:39Z"},{"alias_kind":"pith_short_12","alias_value":"S7T6SKUPIZNJ","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"S7T6SKUPIZNJD4QB","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"S7T6SKUP","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:S7T6SKUPIZNJD4QBESFYCXKSMX","target":"record","payload":{"canonical_record":{"source":{"id":"1804.04338","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-12T06:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"3f9fe797a715fbc7e0c15a2d6995a4b44c145883f3568eec447bfb633d838e57","abstract_canon_sha256":"1a3551380a61438ecde49c654af54f7d0bc23676720081cb057fd0834d05d984"},"schema_version":"1.0"},"canonical_sha256":"97e7e92a8f465a91f201248b815d5265de2ec5e2a7dc99afb78ffa37fe6ae2b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:39.853267Z","signature_b64":"OSEeU4abJzHMO5XBpwdFVsOwMjjvUSaVg5970rrPAMMjMCAk1OtnIQjwKu2jyJyZI6LEAfpbkTJOYlaJVO1bCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97e7e92a8f465a91f201248b815d5265de2ec5e2a7dc99afb78ffa37fe6ae2b8","last_reissued_at":"2026-05-18T00:18:39.852787Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:39.852787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.04338","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":"2026-05-18T00:18:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BQ+vx3euaUU3TtTOk2ttpAHfoliiT7MJ0hE9D49Vwv/+K68reu5LjqwLaeHbFgJWIO2t/HNVHFglcVPMTXrrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T14:03:48.311331Z"},"content_sha256":"e5c16567bee3116198073e5bd532bdad3daf5489e04fbdc34d404d962afcdfba","schema_version":"1.0","event_id":"sha256:e5c16567bee3116198073e5bd532bdad3daf5489e04fbdc34d404d962afcdfba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:S7T6SKUPIZNJD4QBESFYCXKSMX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MelanoGANs: High Resolution Skin Lesion Synthesis with GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christoph Baur, Nassir Navab, Shadi Albarqouni","submitted_at":"2018-04-12T06:18:31Z","abstract_excerpt":"Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images. Unfortunately, they usually require large training datasets, which are often scarce in the medical field, and to the best of our knowledge GANs have been only applied for medical image synthesis at fairly low resolution. However, many state-of-the-art machine learning models operate on high resolution data as such data carries indispensable, valuable information. In this work, we try to generate realistically looking high resolution images of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.04338","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":""},"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":"2026-05-18T00:18:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+T05xoWQXcHP38HOTGiXXdbS4tyvkJuVoqjcXET0ktT2OjOxRxb3DpiPlOD1Z9KMWaKmbR7L1x8MxZ7yFE/2AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T14:03:48.311674Z"},"content_sha256":"8211759ba50f673f5e42c3c994dff31d5b8aa24f05f8e0fd51955993ec50ff1e","schema_version":"1.0","event_id":"sha256:8211759ba50f673f5e42c3c994dff31d5b8aa24f05f8e0fd51955993ec50ff1e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S7T6SKUPIZNJD4QBESFYCXKSMX/bundle.json","state_url":"https://pith.science/pith/S7T6SKUPIZNJD4QBESFYCXKSMX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S7T6SKUPIZNJD4QBESFYCXKSMX/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":"2026-06-27T14:03:48Z","links":{"resolver":"https://pith.science/pith/S7T6SKUPIZNJD4QBESFYCXKSMX","bundle":"https://pith.science/pith/S7T6SKUPIZNJD4QBESFYCXKSMX/bundle.json","state":"https://pith.science/pith/S7T6SKUPIZNJD4QBESFYCXKSMX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S7T6SKUPIZNJD4QBESFYCXKSMX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:S7T6SKUPIZNJD4QBESFYCXKSMX","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":"1a3551380a61438ecde49c654af54f7d0bc23676720081cb057fd0834d05d984","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-12T06:18:31Z","title_canon_sha256":"3f9fe797a715fbc7e0c15a2d6995a4b44c145883f3568eec447bfb633d838e57"},"schema_version":"1.0","source":{"id":"1804.04338","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.04338","created_at":"2026-05-18T00:18:39Z"},{"alias_kind":"arxiv_version","alias_value":"1804.04338v1","created_at":"2026-05-18T00:18:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.04338","created_at":"2026-05-18T00:18:39Z"},{"alias_kind":"pith_short_12","alias_value":"S7T6SKUPIZNJ","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"S7T6SKUPIZNJD4QB","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"S7T6SKUP","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:8211759ba50f673f5e42c3c994dff31d5b8aa24f05f8e0fd51955993ec50ff1e","target":"graph","created_at":"2026-05-18T00:18:39Z","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"},"paper":{"abstract_excerpt":"Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images. Unfortunately, they usually require large training datasets, which are often scarce in the medical field, and to the best of our knowledge GANs have been only applied for medical image synthesis at fairly low resolution. However, many state-of-the-art machine learning models operate on high resolution data as such data carries indispensable, valuable information. In this work, we try to generate realistically looking high resolution images of ","authors_text":"Christoph Baur, Nassir Navab, Shadi Albarqouni","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-12T06:18:31Z","title":"MelanoGANs: High Resolution Skin Lesion Synthesis with GANs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.04338","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:e5c16567bee3116198073e5bd532bdad3daf5489e04fbdc34d404d962afcdfba","target":"record","created_at":"2026-05-18T00:18:39Z","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":"1a3551380a61438ecde49c654af54f7d0bc23676720081cb057fd0834d05d984","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-12T06:18:31Z","title_canon_sha256":"3f9fe797a715fbc7e0c15a2d6995a4b44c145883f3568eec447bfb633d838e57"},"schema_version":"1.0","source":{"id":"1804.04338","kind":"arxiv","version":1}},"canonical_sha256":"97e7e92a8f465a91f201248b815d5265de2ec5e2a7dc99afb78ffa37fe6ae2b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"97e7e92a8f465a91f201248b815d5265de2ec5e2a7dc99afb78ffa37fe6ae2b8","first_computed_at":"2026-05-18T00:18:39.852787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:39.852787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OSEeU4abJzHMO5XBpwdFVsOwMjjvUSaVg5970rrPAMMjMCAk1OtnIQjwKu2jyJyZI6LEAfpbkTJOYlaJVO1bCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:39.853267Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.04338","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5c16567bee3116198073e5bd532bdad3daf5489e04fbdc34d404d962afcdfba","sha256:8211759ba50f673f5e42c3c994dff31d5b8aa24f05f8e0fd51955993ec50ff1e"],"state_sha256":"cc9cc6064ced532c91940491003d02c83e91b2d7aea233776d3eb0fdf4d4e0ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QxYWzs/DosKdUSSGk7FFgXIc8wQxVAFg10N7vlnstjMXIdq8q0ZZhkGEkSbQ0+eFlG+V0/xRmf4czkg0txoUCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T14:03:48.313576Z","bundle_sha256":"175e56a99346b891ad9e7a8af42cf86f131e2de51caff2435a20ff0418a51dd1"}}