{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EDUHDEI4VLMO2HIAXHVGI2T3BG","short_pith_number":"pith:EDUHDEI4","schema_version":"1.0","canonical_sha256":"20e871911caad8ed1d00b9ea646a7b09ab287cb9304c063cca0c4883880012f4","source":{"kind":"arxiv","id":"2303.17636","version":1},"attestation_state":"computed","paper":{"title":"Whether and When does Endoscopy Domain Pretraining Make Sense?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dominik Bati\\'c, Ege \\\"Ozsoy, Felix Holm, Nassir Navab, Tobias Czempiel","submitted_at":"2023-03-30T18:01:26Z","abstract_excerpt":"Automated endoscopy video analysis is a challenging task in medical computer vision, with the primary objective of assisting surgeons during procedures. The difficulty arises from the complexity of surgical scenes and the lack of a sufficient amount of annotated data. In recent years, large-scale pretraining has shown great success in natural language processing and computer vision communities. These approaches reduce the need for annotated data, which is always a concern in the medical domain. However, most works on endoscopic video understanding use models pretrained on natural images, creat"},"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":"2303.17636","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-30T18:01:26Z","cross_cats_sorted":[],"title_canon_sha256":"9e0033e2eb5aea45261d06de9622569d99742dcd5c61f579ee8d3358249b6d02","abstract_canon_sha256":"0bc2b6fbe39d1b6d0123b07ad350f4b21ee7c81c396bd870dd0e4783269a1ee9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:56:43.397465Z","signature_b64":"xUCt/S4Ve3Djya1iRP+AiqBxf1EVUvUcCVw8Y4H3Yfq2z49DA0GBb3pv9Q9rU3bDfwHMwFYjfTakWVGuV8s/AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20e871911caad8ed1d00b9ea646a7b09ab287cb9304c063cca0c4883880012f4","last_reissued_at":"2026-07-05T05:56:43.396969Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:56:43.396969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Whether and When does Endoscopy Domain Pretraining Make Sense?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dominik Bati\\'c, Ege \\\"Ozsoy, Felix Holm, Nassir Navab, Tobias Czempiel","submitted_at":"2023-03-30T18:01:26Z","abstract_excerpt":"Automated endoscopy video analysis is a challenging task in medical computer vision, with the primary objective of assisting surgeons during procedures. The difficulty arises from the complexity of surgical scenes and the lack of a sufficient amount of annotated data. In recent years, large-scale pretraining has shown great success in natural language processing and computer vision communities. These approaches reduce the need for annotated data, which is always a concern in the medical domain. However, most works on endoscopic video understanding use models pretrained on natural images, creat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.17636","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/2303.17636/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":"2303.17636","created_at":"2026-07-05T05:56:43.397031+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.17636v1","created_at":"2026-07-05T05:56:43.397031+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.17636","created_at":"2026-07-05T05:56:43.397031+00:00"},{"alias_kind":"pith_short_12","alias_value":"EDUHDEI4VLMO","created_at":"2026-07-05T05:56:43.397031+00:00"},{"alias_kind":"pith_short_16","alias_value":"EDUHDEI4VLMO2HIA","created_at":"2026-07-05T05:56:43.397031+00:00"},{"alias_kind":"pith_short_8","alias_value":"EDUHDEI4","created_at":"2026-07-05T05:56:43.397031+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/EDUHDEI4VLMO2HIAXHVGI2T3BG","json":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG.json","graph_json":"https://pith.science/api/pith-number/EDUHDEI4VLMO2HIAXHVGI2T3BG/graph.json","events_json":"https://pith.science/api/pith-number/EDUHDEI4VLMO2HIAXHVGI2T3BG/events.json","paper":"https://pith.science/paper/EDUHDEI4"},"agent_actions":{"view_html":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG","download_json":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG.json","view_paper":"https://pith.science/paper/EDUHDEI4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.17636&json=true","fetch_graph":"https://pith.science/api/pith-number/EDUHDEI4VLMO2HIAXHVGI2T3BG/graph.json","fetch_events":"https://pith.science/api/pith-number/EDUHDEI4VLMO2HIAXHVGI2T3BG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG/action/storage_attestation","attest_author":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG/action/author_attestation","sign_citation":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG/action/citation_signature","submit_replication":"https://pith.science/pith/EDUHDEI4VLMO2HIAXHVGI2T3BG/action/replication_record"}},"created_at":"2026-07-05T05:56:43.397031+00:00","updated_at":"2026-07-05T05:56:43.397031+00:00"}