{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RZ7TSBUQG45VTKTWSAPDCJ4QQY","short_pith_number":"pith:RZ7TSBUQ","schema_version":"1.0","canonical_sha256":"8e7f390690373b59aa76901e3127908601a58c861200e0737ea24ce8b6995758","source":{"kind":"arxiv","id":"1711.06606","version":2},"attestation_state":"computed","paper":{"title":"Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Faisal Mahmood, Nicholas J. Durr, Richard Chen","submitted_at":"2017-11-17T16:02:37Z","abstract_excerpt":"To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire because labeled medical images are not usually available due to privacy issues, lack of experts available for annotation, underrepresentation of rare conditions and poor standardization. Lack of annotated data has been addressed in conventional vision applications using synthetic images refined via unsupervised adversarial training to look like real images. However, this approach is difficult to extend to general medical imaging because "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1711.06606","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-17T16:02:37Z","cross_cats_sorted":[],"title_canon_sha256":"ab552d729be2185a1ade7a62230925e0da519e7b3949c221749a48e9785fbe21","abstract_canon_sha256":"0b2d23d5e2201d1cd2c094ecbcf42712f354b04b48b507d849b26095481ce77b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:09.115764Z","signature_b64":"m2g2tukCGFk6F7Xxmr+Z0PSURaQmOz/TSqPtmln2olgXWzodccuHXAfWxdEatp07uI9XEsQUY53ngXQORU0mAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e7f390690373b59aa76901e3127908601a58c861200e0737ea24ce8b6995758","last_reissued_at":"2026-05-18T00:12:09.115288Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:09.115288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Faisal Mahmood, Nicholas J. Durr, Richard Chen","submitted_at":"2017-11-17T16:02:37Z","abstract_excerpt":"To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire because labeled medical images are not usually available due to privacy issues, lack of experts available for annotation, underrepresentation of rare conditions and poor standardization. Lack of annotated data has been addressed in conventional vision applications using synthetic images refined via unsupervised adversarial training to look like real images. However, this approach is difficult to extend to general medical imaging because "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06606","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.06606","created_at":"2026-05-18T00:12:09.115373+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.06606v2","created_at":"2026-05-18T00:12:09.115373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06606","created_at":"2026-05-18T00:12:09.115373+00:00"},{"alias_kind":"pith_short_12","alias_value":"RZ7TSBUQG45V","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"RZ7TSBUQG45VTKTW","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"RZ7TSBUQ","created_at":"2026-05-18T12:31:43.269735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY","json":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY.json","graph_json":"https://pith.science/api/pith-number/RZ7TSBUQG45VTKTWSAPDCJ4QQY/graph.json","events_json":"https://pith.science/api/pith-number/RZ7TSBUQG45VTKTWSAPDCJ4QQY/events.json","paper":"https://pith.science/paper/RZ7TSBUQ"},"agent_actions":{"view_html":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY","download_json":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY.json","view_paper":"https://pith.science/paper/RZ7TSBUQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.06606&json=true","fetch_graph":"https://pith.science/api/pith-number/RZ7TSBUQG45VTKTWSAPDCJ4QQY/graph.json","fetch_events":"https://pith.science/api/pith-number/RZ7TSBUQG45VTKTWSAPDCJ4QQY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY/action/storage_attestation","attest_author":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY/action/author_attestation","sign_citation":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY/action/citation_signature","submit_replication":"https://pith.science/pith/RZ7TSBUQG45VTKTWSAPDCJ4QQY/action/replication_record"}},"created_at":"2026-05-18T00:12:09.115373+00:00","updated_at":"2026-05-18T00:12:09.115373+00:00"}