{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:KPLH6MWRJNHJVYH4J5Y23XYQ4T","short_pith_number":"pith:KPLH6MWR","schema_version":"1.0","canonical_sha256":"53d67f32d14b4e9ae0fc4f71addf10e4fb9eadd37d467f8d497e6eed9919b66f","source":{"kind":"arxiv","id":"1810.10850","version":2},"attestation_state":"computed","paper":{"title":"An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hayit Greenspan, Jiexiang Wang, John Paisley, Liyan Sun, Xinghao Ding, Yue Huang","submitted_at":"2018-10-25T12:57:31Z","abstract_excerpt":"The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike t"},"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":"1810.10850","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-25T12:57:31Z","cross_cats_sorted":[],"title_canon_sha256":"575e578252ae77e570ce37be1f23df40e947a8f6702112e89c193edbb353e3a1","abstract_canon_sha256":"ec4b3915c81c6a0c92286f82216d86ebda92a1d04bb14f50a06af541a3e67c32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:11.363757Z","signature_b64":"K/DvBU1l+AmTSGEt2ik9vD9MqD7x51Tq8oWtw1xJO2LRwU8yfhbCTXb9WNeFdk0H2R/Imbg4BsMe/P/tTVhSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53d67f32d14b4e9ae0fc4f71addf10e4fb9eadd37d467f8d497e6eed9919b66f","last_reissued_at":"2026-05-17T23:49:11.363110Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:11.363110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hayit Greenspan, Jiexiang Wang, John Paisley, Liyan Sun, Xinghao Ding, Yue Huang","submitted_at":"2018-10-25T12:57:31Z","abstract_excerpt":"The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10850","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":"1810.10850","created_at":"2026-05-17T23:49:11.363230+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.10850v2","created_at":"2026-05-17T23:49:11.363230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10850","created_at":"2026-05-17T23:49:11.363230+00:00"},{"alias_kind":"pith_short_12","alias_value":"KPLH6MWRJNHJ","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"KPLH6MWRJNHJVYH4","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"KPLH6MWR","created_at":"2026-05-18T12:32:33.847187+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/KPLH6MWRJNHJVYH4J5Y23XYQ4T","json":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T.json","graph_json":"https://pith.science/api/pith-number/KPLH6MWRJNHJVYH4J5Y23XYQ4T/graph.json","events_json":"https://pith.science/api/pith-number/KPLH6MWRJNHJVYH4J5Y23XYQ4T/events.json","paper":"https://pith.science/paper/KPLH6MWR"},"agent_actions":{"view_html":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T","download_json":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T.json","view_paper":"https://pith.science/paper/KPLH6MWR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.10850&json=true","fetch_graph":"https://pith.science/api/pith-number/KPLH6MWRJNHJVYH4J5Y23XYQ4T/graph.json","fetch_events":"https://pith.science/api/pith-number/KPLH6MWRJNHJVYH4J5Y23XYQ4T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T/action/storage_attestation","attest_author":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T/action/author_attestation","sign_citation":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T/action/citation_signature","submit_replication":"https://pith.science/pith/KPLH6MWRJNHJVYH4J5Y23XYQ4T/action/replication_record"}},"created_at":"2026-05-17T23:49:11.363230+00:00","updated_at":"2026-05-17T23:49:11.363230+00:00"}