{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LPTMYMF4NEXZWHL4BCD4W3QDSX","short_pith_number":"pith:LPTMYMF4","schema_version":"1.0","canonical_sha256":"5be6cc30bc692f9b1d7c0887cb6e0395ce67c6db6e5c4b41ae71ae2643d07683","source":{"kind":"arxiv","id":"1905.12883","version":1},"attestation_state":"computed","paper":{"title":"P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingzhe Wu, Caihong Zeng, Guangyu Sun, Shiwan Zhao, Xiaolu Zhang, Zhihong Liu, Zhong Su","submitted_at":"2019-05-30T07:07:29Z","abstract_excerpt":"Recently, deep convolutional neural networks (CNNs) have achieved great success in pathological image classification. However, due to the limited number of labeled pathological images, there are still two challenges to be addressed: (1) overfitting: the performance of a CNN model is undermined by the overfitting due to its huge amounts of parameters and the insufficiency of labeled training data. (2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset. The smaller the dataset, the worse the priv"},"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":"1905.12883","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-30T07:07:29Z","cross_cats_sorted":[],"title_canon_sha256":"f452ff9520b6e299ff308ca3d1aa7facaca4b09822492850d64af4aa8e7b35d3","abstract_canon_sha256":"e0d608381994bcb2fb573edb774a0ae93f5e7897a556e1d4da0603a62d034722"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:39.411522Z","signature_b64":"MwXKoqc04ek8cgK15X6Vs0UBtYyjY6lenDMZf3i1IUQFhytmumSA7DS1MYZ2D2OXihsge1epKveq+VCu7UUUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5be6cc30bc692f9b1d7c0887cb6e0395ce67c6db6e5c4b41ae71ae2643d07683","last_reissued_at":"2026-05-17T23:44:39.411041Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:39.411041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingzhe Wu, Caihong Zeng, Guangyu Sun, Shiwan Zhao, Xiaolu Zhang, Zhihong Liu, Zhong Su","submitted_at":"2019-05-30T07:07:29Z","abstract_excerpt":"Recently, deep convolutional neural networks (CNNs) have achieved great success in pathological image classification. However, due to the limited number of labeled pathological images, there are still two challenges to be addressed: (1) overfitting: the performance of a CNN model is undermined by the overfitting due to its huge amounts of parameters and the insufficiency of labeled training data. (2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset. The smaller the dataset, the worse the priv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.12883","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1905.12883","created_at":"2026-05-17T23:44:39.411128+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.12883v1","created_at":"2026-05-17T23:44:39.411128+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.12883","created_at":"2026-05-17T23:44:39.411128+00:00"},{"alias_kind":"pith_short_12","alias_value":"LPTMYMF4NEXZ","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"LPTMYMF4NEXZWHL4","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"LPTMYMF4","created_at":"2026-05-18T12:33:21.387695+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/LPTMYMF4NEXZWHL4BCD4W3QDSX","json":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX.json","graph_json":"https://pith.science/api/pith-number/LPTMYMF4NEXZWHL4BCD4W3QDSX/graph.json","events_json":"https://pith.science/api/pith-number/LPTMYMF4NEXZWHL4BCD4W3QDSX/events.json","paper":"https://pith.science/paper/LPTMYMF4"},"agent_actions":{"view_html":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX","download_json":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX.json","view_paper":"https://pith.science/paper/LPTMYMF4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.12883&json=true","fetch_graph":"https://pith.science/api/pith-number/LPTMYMF4NEXZWHL4BCD4W3QDSX/graph.json","fetch_events":"https://pith.science/api/pith-number/LPTMYMF4NEXZWHL4BCD4W3QDSX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX/action/storage_attestation","attest_author":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX/action/author_attestation","sign_citation":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX/action/citation_signature","submit_replication":"https://pith.science/pith/LPTMYMF4NEXZWHL4BCD4W3QDSX/action/replication_record"}},"created_at":"2026-05-17T23:44:39.411128+00:00","updated_at":"2026-05-17T23:44:39.411128+00:00"}