{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GTLQX47465BRIVWBGLFV2KZX24","short_pith_number":"pith:GTLQX474","canonical_record":{"source":{"id":"1807.10225","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T16:25:18Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"f3ebe368510635e89e0d2363effb3e305c5b84ec5e8dfa0154545244ef7a33ef","abstract_canon_sha256":"1d6ded068cec8ac23a87b707b5b5f5b338496892a4b6fa29743aafcf976833c8"},"schema_version":"1.0"},"canonical_sha256":"34d70bf3fcf7431456c132cb5d2b37d7051a14413a577c65dc4fa2245127dd4d","source":{"kind":"arxiv","id":"1807.10225","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10225","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10225v2","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10225","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"pith_short_12","alias_value":"GTLQX47465BR","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GTLQX47465BRIVWB","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GTLQX474","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GTLQX47465BRIVWBGLFV2KZX24","target":"record","payload":{"canonical_record":{"source":{"id":"1807.10225","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T16:25:18Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"f3ebe368510635e89e0d2363effb3e305c5b84ec5e8dfa0154545244ef7a33ef","abstract_canon_sha256":"1d6ded068cec8ac23a87b707b5b5f5b338496892a4b6fa29743aafcf976833c8"},"schema_version":"1.0"},"canonical_sha256":"34d70bf3fcf7431456c132cb5d2b37d7051a14413a577c65dc4fa2245127dd4d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:45.197750Z","signature_b64":"UZBe5zZzXPUSDdORu3iQj6ofSjjuvq0gnbzLUzR8VMk+PpLXQ2O+NvFKylc8PCGBNjfNWkf9YF9kpx6xjFzRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34d70bf3fcf7431456c132cb5d2b37d7051a14413a577c65dc4fa2245127dd4d","last_reissued_at":"2026-05-18T00:05:45.197005Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:45.197005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.10225","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":"2026-05-18T00:05:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dJKIE10iJwLkJSeOUMxA+fm2O6vZ/esrpGnX5/iW6jueOHEV7NXdL7NlLV2gCyfcNJfpxpqgji4aSiiLtYiuCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T01:16:58.889747Z"},"content_sha256":"627ea54711bc69c7d479ae41f22c85e0a992c3844f7efcc7ae71b346b094e43f","schema_version":"1.0","event_id":"sha256:627ea54711bc69c7d479ae41f22c85e0a992c3844f7efcc7ae71b346b094e43f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GTLQX47465BRIVWBGLFV2KZX24","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Christopher G Schwarz, Hoo-Chang Shin, Jameson K Rogers, Jeffrey L Gunter, Katherine Andriole, Mark Michalski, Matthew L Senjem, Neil A Tenenholtz","submitted_at":"2018-07-26T16:25:18Z","abstract_excerpt":"Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10225","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":""},"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":"2026-05-18T00:05:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ogekj/zA6M4pCmTJGMxg43AkY6gFdewnrUELW6pHccyGLuhXcZBQa/l0emcSKcZl1w3oBNtC+pgxUwx0bi1kCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T01:16:58.890093Z"},"content_sha256":"4c134d82ca3bf731993ba29cedb0f775dd7220f4a47f6000d3edc0f8a8736005","schema_version":"1.0","event_id":"sha256:4c134d82ca3bf731993ba29cedb0f775dd7220f4a47f6000d3edc0f8a8736005"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GTLQX47465BRIVWBGLFV2KZX24/bundle.json","state_url":"https://pith.science/pith/GTLQX47465BRIVWBGLFV2KZX24/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GTLQX47465BRIVWBGLFV2KZX24/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":"2026-07-23T01:16:58Z","links":{"resolver":"https://pith.science/pith/GTLQX47465BRIVWBGLFV2KZX24","bundle":"https://pith.science/pith/GTLQX47465BRIVWBGLFV2KZX24/bundle.json","state":"https://pith.science/pith/GTLQX47465BRIVWBGLFV2KZX24/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GTLQX47465BRIVWBGLFV2KZX24/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GTLQX47465BRIVWBGLFV2KZX24","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":"1d6ded068cec8ac23a87b707b5b5f5b338496892a4b6fa29743aafcf976833c8","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T16:25:18Z","title_canon_sha256":"f3ebe368510635e89e0d2363effb3e305c5b84ec5e8dfa0154545244ef7a33ef"},"schema_version":"1.0","source":{"id":"1807.10225","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10225","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10225v2","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10225","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"pith_short_12","alias_value":"GTLQX47465BR","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GTLQX47465BRIVWB","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GTLQX474","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:4c134d82ca3bf731993ba29cedb0f775dd7220f4a47f6000d3edc0f8a8736005","target":"graph","created_at":"2026-05-18T00:05:45Z","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":"Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images","authors_text":"Christopher G Schwarz, Hoo-Chang Shin, Jameson K Rogers, Jeffrey L Gunter, Katherine Andriole, Mark Michalski, Matthew L Senjem, Neil A Tenenholtz","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T16:25:18Z","title":"Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10225","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":"sha256:627ea54711bc69c7d479ae41f22c85e0a992c3844f7efcc7ae71b346b094e43f","target":"record","created_at":"2026-05-18T00:05:45Z","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":"1d6ded068cec8ac23a87b707b5b5f5b338496892a4b6fa29743aafcf976833c8","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T16:25:18Z","title_canon_sha256":"f3ebe368510635e89e0d2363effb3e305c5b84ec5e8dfa0154545244ef7a33ef"},"schema_version":"1.0","source":{"id":"1807.10225","kind":"arxiv","version":2}},"canonical_sha256":"34d70bf3fcf7431456c132cb5d2b37d7051a14413a577c65dc4fa2245127dd4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34d70bf3fcf7431456c132cb5d2b37d7051a14413a577c65dc4fa2245127dd4d","first_computed_at":"2026-05-18T00:05:45.197005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:45.197005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UZBe5zZzXPUSDdORu3iQj6ofSjjuvq0gnbzLUzR8VMk+PpLXQ2O+NvFKylc8PCGBNjfNWkf9YF9kpx6xjFzRDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:45.197750Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.10225","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:627ea54711bc69c7d479ae41f22c85e0a992c3844f7efcc7ae71b346b094e43f","sha256:4c134d82ca3bf731993ba29cedb0f775dd7220f4a47f6000d3edc0f8a8736005"],"state_sha256":"d8e37d3ca615a2ad7a7b81bd01a9761bd1f8b8e21e867986f2672e9ef9601073"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AdqV8SE1bii4whM9/tMV6APQe2LI51A8GLRpu5TZdWBHSQi62MqdgsWLCziK7pLHo2aU8cSc5P4MHrTrp42SAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-23T01:16:58.892159Z","bundle_sha256":"9e227e659cbb24cf9d35c2c322b8b04a601a845a1f3a9aba0b8a6849b568489f"}}