{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:J6HX7PSXADUV5UBMYCBIVASBK2","short_pith_number":"pith:J6HX7PSX","schema_version":"1.0","canonical_sha256":"4f8f7fbe5700e95ed02cc0828a82415692c0650538dd61b3c7536f8961bb95c6","source":{"kind":"arxiv","id":"2308.08746","version":2},"attestation_state":"computed","paper":{"title":"SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Jiebo Luo, Jing Zhang, Kun Hu, Wenxi Yue, Yong Xia, Zhiyong Wang","submitted_at":"2023-08-17T02:51:01Z","abstract_excerpt":"The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to inferior generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing "},"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":"2308.08746","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-17T02:51:01Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"559d4d156a35898795e78b4ac1215139f5faa6ba56b7a9949639fa540ba46102","abstract_canon_sha256":"3a285d7f816677a2efcb590ed8591bc492882c69c52cf41f44d12edfbdf9364a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:39.250271Z","signature_b64":"vMus7qOxb6zdcJDAY4a4Boj9eTVFNZ2N4twaysNp/a8IFM/mr0qlOjJHU2+44P9IT4U0xzrK124WKc5NMzleBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f8f7fbe5700e95ed02cc0828a82415692c0650538dd61b3c7536f8961bb95c6","last_reissued_at":"2026-07-05T07:26:39.249792Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:39.249792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Jiebo Luo, Jing Zhang, Kun Hu, Wenxi Yue, Yong Xia, Zhiyong Wang","submitted_at":"2023-08-17T02:51:01Z","abstract_excerpt":"The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to inferior generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.08746","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/2308.08746/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":"2308.08746","created_at":"2026-07-05T07:26:39.249853+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.08746v2","created_at":"2026-07-05T07:26:39.249853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.08746","created_at":"2026-07-05T07:26:39.249853+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6HX7PSXADUV","created_at":"2026-07-05T07:26:39.249853+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6HX7PSXADUV5UBM","created_at":"2026-07-05T07:26:39.249853+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6HX7PSX","created_at":"2026-07-05T07:26:39.249853+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09562","citing_title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","ref_index":74,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2","json":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2.json","graph_json":"https://pith.science/api/pith-number/J6HX7PSXADUV5UBMYCBIVASBK2/graph.json","events_json":"https://pith.science/api/pith-number/J6HX7PSXADUV5UBMYCBIVASBK2/events.json","paper":"https://pith.science/paper/J6HX7PSX"},"agent_actions":{"view_html":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2","download_json":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2.json","view_paper":"https://pith.science/paper/J6HX7PSX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.08746&json=true","fetch_graph":"https://pith.science/api/pith-number/J6HX7PSXADUV5UBMYCBIVASBK2/graph.json","fetch_events":"https://pith.science/api/pith-number/J6HX7PSXADUV5UBMYCBIVASBK2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2/action/storage_attestation","attest_author":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2/action/author_attestation","sign_citation":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2/action/citation_signature","submit_replication":"https://pith.science/pith/J6HX7PSXADUV5UBMYCBIVASBK2/action/replication_record"}},"created_at":"2026-07-05T07:26:39.249853+00:00","updated_at":"2026-07-05T07:26:39.249853+00:00"}