{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:G6PGGTYSBIWZ36L5WXFJ2GNHAM","short_pith_number":"pith:G6PGGTYS","schema_version":"1.0","canonical_sha256":"379e634f120a2d9df97db5ca9d19a703150ca62a7baa1d829a9bb0035769e1cf","source":{"kind":"arxiv","id":"2206.11485","version":2},"attestation_state":"computed","paper":{"title":"Patient Aware Active Learning for Fine-Grained OCT Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Ahmad Mustafa, Ghassan AlRegib, Gukyeong Kwon, Ryan Benkert, Yash-yee Logan","submitted_at":"2022-06-23T05:47:51Z","abstract_excerpt":"This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing frameworks have primarily used mathematical constructs to engineer uncertainty or diversity-based methods for selecting the most informative samples. However, such algorithms do not present themselves naturally as usable by the medical community and healthcare providers. Thus, their deployment in clinical settings is very limited, if any. For this purpose, we propose a framework that incorporates clinical insights into"},"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":"2206.11485","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-06-23T05:47:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"adeae8e0bc6fa222905af026bf0f88db2ee3b6771b6df9da7cc943d74a45e2af","abstract_canon_sha256":"e013085c98432e5478e3d4eeef8961c2d545b58639356c26c4be806e4350979c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:34:55.429500Z","signature_b64":"Gcl17gak46JUM/LAlNN//JbFhMvOGaKeZUZAmPP1TWWqJgZcJxNKCuWPL9U3J55f+31CDvXiWJvdy4laTU4pCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"379e634f120a2d9df97db5ca9d19a703150ca62a7baa1d829a9bb0035769e1cf","last_reissued_at":"2026-07-05T04:34:55.429022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:34:55.429022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Patient Aware Active Learning for Fine-Grained OCT Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Ahmad Mustafa, Ghassan AlRegib, Gukyeong Kwon, Ryan Benkert, Yash-yee Logan","submitted_at":"2022-06-23T05:47:51Z","abstract_excerpt":"This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing frameworks have primarily used mathematical constructs to engineer uncertainty or diversity-based methods for selecting the most informative samples. However, such algorithms do not present themselves naturally as usable by the medical community and healthcare providers. Thus, their deployment in clinical settings is very limited, if any. For this purpose, we propose a framework that incorporates clinical insights into"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.11485","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.11485/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2206.11485","created_at":"2026-07-05T04:34:55.429079+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.11485v2","created_at":"2026-07-05T04:34:55.429079+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.11485","created_at":"2026-07-05T04:34:55.429079+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6PGGTYSBIWZ","created_at":"2026-07-05T04:34:55.429079+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6PGGTYSBIWZ36L5","created_at":"2026-07-05T04:34:55.429079+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6PGGTYS","created_at":"2026-07-05T04:34:55.429079+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.10896","citing_title":"Targeting Negative Flips in Active Learning using Validation Sets","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM","json":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM.json","graph_json":"https://pith.science/api/pith-number/G6PGGTYSBIWZ36L5WXFJ2GNHAM/graph.json","events_json":"https://pith.science/api/pith-number/G6PGGTYSBIWZ36L5WXFJ2GNHAM/events.json","paper":"https://pith.science/paper/G6PGGTYS"},"agent_actions":{"view_html":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM","download_json":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM.json","view_paper":"https://pith.science/paper/G6PGGTYS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.11485&json=true","fetch_graph":"https://pith.science/api/pith-number/G6PGGTYSBIWZ36L5WXFJ2GNHAM/graph.json","fetch_events":"https://pith.science/api/pith-number/G6PGGTYSBIWZ36L5WXFJ2GNHAM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM/action/storage_attestation","attest_author":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM/action/author_attestation","sign_citation":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM/action/citation_signature","submit_replication":"https://pith.science/pith/G6PGGTYSBIWZ36L5WXFJ2GNHAM/action/replication_record"}},"created_at":"2026-07-05T04:34:55.429079+00:00","updated_at":"2026-07-05T04:34:55.429079+00:00"}