{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BPTWBEKLZPMVLK3NCAQFGMBP5D","short_pith_number":"pith:BPTWBEKL","schema_version":"1.0","canonical_sha256":"0be760914bcbd955ab6d102053302fe8d42821e0dd0cf282258dcdd988b25aa4","source":{"kind":"arxiv","id":"2201.11246","version":1},"attestation_state":"computed","paper":{"title":"HistoKT: Cross Knowledge Transfer in Computational Pathology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Angelo Genovese, Corwyn Rowsell, Jiadai Zhu, Konstantinos N. Plataniotis, Lina Chen, Mahdi S. Hosseini, Ryan Zhang, Savvas Damaskinos, Sonal Varma, Stephen Yang","submitted_at":"2022-01-27T00:34:19Z","abstract_excerpt":"The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. %Since pathologist time is expensive, dataset curation is intrinsically difficult. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine 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":"2201.11246","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-27T00:34:19Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"358069c268dccd8909e7e16ae4e5abf2fe447697e88e12b9b04b5f991f022a2c","abstract_canon_sha256":"eb59d9df7841ea4f7cc32d81abe6405fa56f0b83a5b75f1d42d627a24fabe42a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:52:00.011961Z","signature_b64":"6iNeWYu6V1/be+/gnm2aZuvtF9rB0eLGBUml6Mdqf5QwQYF0fLM7MUeg61CLIDqe4HajEx07IVGzMC3QRCu7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0be760914bcbd955ab6d102053302fe8d42821e0dd0cf282258dcdd988b25aa4","last_reissued_at":"2026-07-05T03:52:00.011542Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:52:00.011542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HistoKT: Cross Knowledge Transfer in Computational Pathology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Angelo Genovese, Corwyn Rowsell, Jiadai Zhu, Konstantinos N. Plataniotis, Lina Chen, Mahdi S. Hosseini, Ryan Zhang, Savvas Damaskinos, Sonal Varma, Stephen Yang","submitted_at":"2022-01-27T00:34:19Z","abstract_excerpt":"The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. %Since pathologist time is expensive, dataset curation is intrinsically difficult. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.11246","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/2201.11246/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":"2201.11246","created_at":"2026-07-05T03:52:00.011598+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.11246v1","created_at":"2026-07-05T03:52:00.011598+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.11246","created_at":"2026-07-05T03:52:00.011598+00:00"},{"alias_kind":"pith_short_12","alias_value":"BPTWBEKLZPMV","created_at":"2026-07-05T03:52:00.011598+00:00"},{"alias_kind":"pith_short_16","alias_value":"BPTWBEKLZPMVLK3N","created_at":"2026-07-05T03:52:00.011598+00:00"},{"alias_kind":"pith_short_8","alias_value":"BPTWBEKL","created_at":"2026-07-05T03:52:00.011598+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/BPTWBEKLZPMVLK3NCAQFGMBP5D","json":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D.json","graph_json":"https://pith.science/api/pith-number/BPTWBEKLZPMVLK3NCAQFGMBP5D/graph.json","events_json":"https://pith.science/api/pith-number/BPTWBEKLZPMVLK3NCAQFGMBP5D/events.json","paper":"https://pith.science/paper/BPTWBEKL"},"agent_actions":{"view_html":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D","download_json":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D.json","view_paper":"https://pith.science/paper/BPTWBEKL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.11246&json=true","fetch_graph":"https://pith.science/api/pith-number/BPTWBEKLZPMVLK3NCAQFGMBP5D/graph.json","fetch_events":"https://pith.science/api/pith-number/BPTWBEKLZPMVLK3NCAQFGMBP5D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D/action/storage_attestation","attest_author":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D/action/author_attestation","sign_citation":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D/action/citation_signature","submit_replication":"https://pith.science/pith/BPTWBEKLZPMVLK3NCAQFGMBP5D/action/replication_record"}},"created_at":"2026-07-05T03:52:00.011598+00:00","updated_at":"2026-07-05T03:52:00.011598+00:00"}