{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5CHOSCM5PMMGH4JZ42PLOS4P7Q","short_pith_number":"pith:5CHOSCM5","schema_version":"1.0","canonical_sha256":"e88ee9099d7b1863f139e69eb74b8ffc27f494f0338100d454fd0f98eea2c13e","source":{"kind":"arxiv","id":"2409.12467","version":2},"attestation_state":"computed","paper":{"title":"SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongbin Liu, Hongliang Ren, Jinlin Wu, Long Bai, Sebastien Ourselin, Xingjian Luo, Zhen Chen, Zhen Lei","submitted_at":"2024-09-19T05:08:33Z","abstract_excerpt":"Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, they formulated the task as a series of frame-wise classification, which resulted in a lack of global context of the entire procedure and incoherent predictions. Moreover, besides online analysis, accurate offline surgical phase recognition is also in significant clinical need for retrospective analysis, and existing online algorithms do not fully a"},"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":"2409.12467","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-19T05:08:33Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c2bd3d78937f9324537174546ea9ad366304858cbeb4230e52b18fc2336df960","abstract_canon_sha256":"c01eb18e58afbfe7d40b346489bef70a6a5ee0a4a1187055c02b221e48f620e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:05.516497Z","signature_b64":"TTYr7vtEuEa4wAqjQJpGeWRg1gOmjKCEv5EdolNGo3GBTSBrT/ccTIGx4ga06psv8bf62ArEDJ09tck85MVjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e88ee9099d7b1863f139e69eb74b8ffc27f494f0338100d454fd0f98eea2c13e","last_reissued_at":"2026-07-05T10:14:05.516006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:05.516006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongbin Liu, Hongliang Ren, Jinlin Wu, Long Bai, Sebastien Ourselin, Xingjian Luo, Zhen Chen, Zhen Lei","submitted_at":"2024-09-19T05:08:33Z","abstract_excerpt":"Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, they formulated the task as a series of frame-wise classification, which resulted in a lack of global context of the entire procedure and incoherent predictions. Moreover, besides online analysis, accurate offline surgical phase recognition is also in significant clinical need for retrospective analysis, and existing online algorithms do not fully a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.12467","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/2409.12467/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":"2409.12467","created_at":"2026-07-05T10:14:05.516064+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.12467v2","created_at":"2026-07-05T10:14:05.516064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.12467","created_at":"2026-07-05T10:14:05.516064+00:00"},{"alias_kind":"pith_short_12","alias_value":"5CHOSCM5PMMG","created_at":"2026-07-05T10:14:05.516064+00:00"},{"alias_kind":"pith_short_16","alias_value":"5CHOSCM5PMMGH4JZ","created_at":"2026-07-05T10:14:05.516064+00:00"},{"alias_kind":"pith_short_8","alias_value":"5CHOSCM5","created_at":"2026-07-05T10:14:05.516064+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16387","citing_title":"Stabilizing Temporal Inference Dynamics for Online Surgical Phase Recognition","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q","json":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q.json","graph_json":"https://pith.science/api/pith-number/5CHOSCM5PMMGH4JZ42PLOS4P7Q/graph.json","events_json":"https://pith.science/api/pith-number/5CHOSCM5PMMGH4JZ42PLOS4P7Q/events.json","paper":"https://pith.science/paper/5CHOSCM5"},"agent_actions":{"view_html":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q","download_json":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q.json","view_paper":"https://pith.science/paper/5CHOSCM5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.12467&json=true","fetch_graph":"https://pith.science/api/pith-number/5CHOSCM5PMMGH4JZ42PLOS4P7Q/graph.json","fetch_events":"https://pith.science/api/pith-number/5CHOSCM5PMMGH4JZ42PLOS4P7Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q/action/storage_attestation","attest_author":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q/action/author_attestation","sign_citation":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q/action/citation_signature","submit_replication":"https://pith.science/pith/5CHOSCM5PMMGH4JZ42PLOS4P7Q/action/replication_record"}},"created_at":"2026-07-05T10:14:05.516064+00:00","updated_at":"2026-07-05T10:14:05.516064+00:00"}