{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CP6CWLEQ25PAUXOPZ2UVMU55DI","short_pith_number":"pith:CP6CWLEQ","schema_version":"1.0","canonical_sha256":"13fc2b2c90d75e0a5dcfcea95653bd1a38b9369c2d7fc5ed9bc3f9387442308d","source":{"kind":"arxiv","id":"2306.02651","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongqiu Wang, Lei Zhu, Yueming Jin","submitted_at":"2023-06-05T07:34:41Z","abstract_excerpt":"For robot-assisted surgery, an accurate surgical report reflects clinical operations during surgery and helps document entry tasks, post-operative analysis and follow-up treatment. It is a challenging task due to many complex and diverse interactions between instruments and tissues in the surgical scene. Although existing surgical report generation methods based on deep learning have achieved large success, they often ignore the interactive relation between tissues and instrumental tools, thereby degrading the report generation performance. This paper presents a neural network to boost surgica"},"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":"2306.02651","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-05T07:34:41Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6ff4682d228bf7af74c122a1c4e9ff901e44c9ba92575bd948addadda9d7d1d7","abstract_canon_sha256":"25429516c6ceedb57873068f04764b486cdb39417751e675769cc16639488311"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:36.589644Z","signature_b64":"PfskxxAPi0nWHJQxheVmXHSLp4zha1fBvWxOLZ3czZpxb85ixlnVpq+RYdbnlys9w7uxztyNcdR+YOBivlnVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13fc2b2c90d75e0a5dcfcea95653bd1a38b9369c2d7fc5ed9bc3f9387442308d","last_reissued_at":"2026-07-05T06:45:36.589136Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:36.589136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongqiu Wang, Lei Zhu, Yueming Jin","submitted_at":"2023-06-05T07:34:41Z","abstract_excerpt":"For robot-assisted surgery, an accurate surgical report reflects clinical operations during surgery and helps document entry tasks, post-operative analysis and follow-up treatment. It is a challenging task due to many complex and diverse interactions between instruments and tissues in the surgical scene. Although existing surgical report generation methods based on deep learning have achieved large success, they often ignore the interactive relation between tissues and instrumental tools, thereby degrading the report generation performance. This paper presents a neural network to boost surgica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.02651","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/2306.02651/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":"2306.02651","created_at":"2026-07-05T06:45:36.589194+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.02651v1","created_at":"2026-07-05T06:45:36.589194+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.02651","created_at":"2026-07-05T06:45:36.589194+00:00"},{"alias_kind":"pith_short_12","alias_value":"CP6CWLEQ25PA","created_at":"2026-07-05T06:45:36.589194+00:00"},{"alias_kind":"pith_short_16","alias_value":"CP6CWLEQ25PAUXOP","created_at":"2026-07-05T06:45:36.589194+00:00"},{"alias_kind":"pith_short_8","alias_value":"CP6CWLEQ","created_at":"2026-07-05T06:45:36.589194+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/CP6CWLEQ25PAUXOPZ2UVMU55DI","json":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI.json","graph_json":"https://pith.science/api/pith-number/CP6CWLEQ25PAUXOPZ2UVMU55DI/graph.json","events_json":"https://pith.science/api/pith-number/CP6CWLEQ25PAUXOPZ2UVMU55DI/events.json","paper":"https://pith.science/paper/CP6CWLEQ"},"agent_actions":{"view_html":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI","download_json":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI.json","view_paper":"https://pith.science/paper/CP6CWLEQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.02651&json=true","fetch_graph":"https://pith.science/api/pith-number/CP6CWLEQ25PAUXOPZ2UVMU55DI/graph.json","fetch_events":"https://pith.science/api/pith-number/CP6CWLEQ25PAUXOPZ2UVMU55DI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI/action/storage_attestation","attest_author":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI/action/author_attestation","sign_citation":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI/action/citation_signature","submit_replication":"https://pith.science/pith/CP6CWLEQ25PAUXOPZ2UVMU55DI/action/replication_record"}},"created_at":"2026-07-05T06:45:36.589194+00:00","updated_at":"2026-07-05T06:45:36.589194+00:00"}