{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2FGZ3OPRDWTAGDSBBINBCTCDIZ","short_pith_number":"pith:2FGZ3OPR","schema_version":"1.0","canonical_sha256":"d14d9db9f11da6030e410a1a114c43466d3fb776fa836529fabd9751f8bd4a3d","source":{"kind":"arxiv","id":"2408.04098","version":2},"attestation_state":"computed","paper":{"title":"Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Hao Ding, Mathias Unberath, Xinyuan Shao, Yiqing Shen","submitted_at":"2024-08-07T21:33:07Z","abstract_excerpt":"Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke, bleeding, and low illumination. Foundation models for image segmentation, such as the segment anything model (SAM) that focuses on interactive prompt-based segmentation, move away from semantic classes and thus can be trained on larger and more diverse data, which offers outstanding zero-shot generalization with appropriate user prompts. Recently, building upon this success, SAM-2 has been proposed to further exten"},"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":"2408.04098","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-08-07T21:33:07Z","cross_cats_sorted":[],"title_canon_sha256":"a892c784406db0b988e5b29d59f3b4f130dd01f76cb050097c6fdaadf5450956","abstract_canon_sha256":"ee5b40db05bdc1d3d51c413d89fb2653f3f37dd9fa0a17f743c87323ff27d2f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:05.720350Z","signature_b64":"Bv7Cmm9zlTOWO+ICHSEom/YOr5xgTzYEYTNFJHJdkeX1AjBn0n8Kpkuo2OkZ0TIez+eCvS3y+f+SlSu9dYVmAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d14d9db9f11da6030e410a1a114c43466d3fb776fa836529fabd9751f8bd4a3d","last_reissued_at":"2026-07-05T08:56:05.719763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:05.719763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Hao Ding, Mathias Unberath, Xinyuan Shao, Yiqing Shen","submitted_at":"2024-08-07T21:33:07Z","abstract_excerpt":"Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke, bleeding, and low illumination. Foundation models for image segmentation, such as the segment anything model (SAM) that focuses on interactive prompt-based segmentation, move away from semantic classes and thus can be trained on larger and more diverse data, which offers outstanding zero-shot generalization with appropriate user prompts. Recently, building upon this success, SAM-2 has been proposed to further exten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.04098","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/2408.04098/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":"2408.04098","created_at":"2026-07-05T08:56:05.719827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.04098v2","created_at":"2026-07-05T08:56:05.719827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.04098","created_at":"2026-07-05T08:56:05.719827+00:00"},{"alias_kind":"pith_short_12","alias_value":"2FGZ3OPRDWTA","created_at":"2026-07-05T08:56:05.719827+00:00"},{"alias_kind":"pith_short_16","alias_value":"2FGZ3OPRDWTAGDSB","created_at":"2026-07-05T08:56:05.719827+00:00"},{"alias_kind":"pith_short_8","alias_value":"2FGZ3OPR","created_at":"2026-07-05T08:56:05.719827+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2407.11906","citing_title":"SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2409.13107","citing_title":"Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models","ref_index":41,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ","json":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ.json","graph_json":"https://pith.science/api/pith-number/2FGZ3OPRDWTAGDSBBINBCTCDIZ/graph.json","events_json":"https://pith.science/api/pith-number/2FGZ3OPRDWTAGDSBBINBCTCDIZ/events.json","paper":"https://pith.science/paper/2FGZ3OPR"},"agent_actions":{"view_html":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ","download_json":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ.json","view_paper":"https://pith.science/paper/2FGZ3OPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.04098&json=true","fetch_graph":"https://pith.science/api/pith-number/2FGZ3OPRDWTAGDSBBINBCTCDIZ/graph.json","fetch_events":"https://pith.science/api/pith-number/2FGZ3OPRDWTAGDSBBINBCTCDIZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ/action/storage_attestation","attest_author":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ/action/author_attestation","sign_citation":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ/action/citation_signature","submit_replication":"https://pith.science/pith/2FGZ3OPRDWTAGDSBBINBCTCDIZ/action/replication_record"}},"created_at":"2026-07-05T08:56:05.719827+00:00","updated_at":"2026-07-05T08:56:05.719827+00:00"}