{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:4XTZIG27AIOLGZFKW63HK7ONUV","short_pith_number":"pith:4XTZIG27","schema_version":"1.0","canonical_sha256":"e5e7941b5f021cb364aab7b6757dcda555bd8e1131d8466c7bf1f37cc9b2cfd7","source":{"kind":"arxiv","id":"2006.11843","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Learning of Deep-Learned Features from Breast Cancer Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","q-bio.QM"],"primary_cat":"eess.IV","authors_text":"Colton Farley, Sanghoon Lee, Simon Shim, Wookjin Choi, Wook-Sung Yoo, Yanjun Zhao","submitted_at":"2020-06-21T16:38:36Z","abstract_excerpt":"Detecting cancer manually in whole slide images requires significant time and effort on the laborious process. Recent advances in whole slide image analysis have stimulated the growth and development of machine learning-based approaches that improve the efficiency and effectiveness in the diagnosis of cancer diseases. In this paper, we propose an unsupervised learning approach for detecting cancer in breast invasive carcinoma (BRCA) whole slide images. The proposed method is fully automated and does not require human involvement during the unsupervised learning procedure. We demonstrate the ef"},"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":"2006.11843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-06-21T16:38:36Z","cross_cats_sorted":["cs.CV","cs.LG","q-bio.QM"],"title_canon_sha256":"194ed68d36115961912cf417d7dadaaa64c687b710fcce8823a80e728af59002","abstract_canon_sha256":"ba44e97ce65e4144b6c5ce90d2f51a0ddbbed741ad934451a3084ed05ab2dc06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:54.306287Z","signature_b64":"BKDynSqeA3CWbvrSBIFWfbuT1iYGfEqefT2JG2DNyAQcBp8cEDg+/pDzEGB0XGS8ITULwdYfxokKgRbcJx5IBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5e7941b5f021cb364aab7b6757dcda555bd8e1131d8466c7bf1f37cc9b2cfd7","last_reissued_at":"2026-07-05T01:11:54.305872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:54.305872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Learning of Deep-Learned Features from Breast Cancer Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","q-bio.QM"],"primary_cat":"eess.IV","authors_text":"Colton Farley, Sanghoon Lee, Simon Shim, Wookjin Choi, Wook-Sung Yoo, Yanjun Zhao","submitted_at":"2020-06-21T16:38:36Z","abstract_excerpt":"Detecting cancer manually in whole slide images requires significant time and effort on the laborious process. Recent advances in whole slide image analysis have stimulated the growth and development of machine learning-based approaches that improve the efficiency and effectiveness in the diagnosis of cancer diseases. In this paper, we propose an unsupervised learning approach for detecting cancer in breast invasive carcinoma (BRCA) whole slide images. The proposed method is fully automated and does not require human involvement during the unsupervised learning procedure. We demonstrate the ef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11843","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.11843/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":"2006.11843","created_at":"2026-07-05T01:11:54.305931+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.11843v1","created_at":"2026-07-05T01:11:54.305931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11843","created_at":"2026-07-05T01:11:54.305931+00:00"},{"alias_kind":"pith_short_12","alias_value":"4XTZIG27AIOL","created_at":"2026-07-05T01:11:54.305931+00:00"},{"alias_kind":"pith_short_16","alias_value":"4XTZIG27AIOLGZFK","created_at":"2026-07-05T01:11:54.305931+00:00"},{"alias_kind":"pith_short_8","alias_value":"4XTZIG27","created_at":"2026-07-05T01:11:54.305931+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/4XTZIG27AIOLGZFKW63HK7ONUV","json":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV.json","graph_json":"https://pith.science/api/pith-number/4XTZIG27AIOLGZFKW63HK7ONUV/graph.json","events_json":"https://pith.science/api/pith-number/4XTZIG27AIOLGZFKW63HK7ONUV/events.json","paper":"https://pith.science/paper/4XTZIG27"},"agent_actions":{"view_html":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV","download_json":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV.json","view_paper":"https://pith.science/paper/4XTZIG27","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.11843&json=true","fetch_graph":"https://pith.science/api/pith-number/4XTZIG27AIOLGZFKW63HK7ONUV/graph.json","fetch_events":"https://pith.science/api/pith-number/4XTZIG27AIOLGZFKW63HK7ONUV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV/action/storage_attestation","attest_author":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV/action/author_attestation","sign_citation":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV/action/citation_signature","submit_replication":"https://pith.science/pith/4XTZIG27AIOLGZFKW63HK7ONUV/action/replication_record"}},"created_at":"2026-07-05T01:11:54.305931+00:00","updated_at":"2026-07-05T01:11:54.305931+00:00"}