{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NZGRZYAVPZQJR6HUP5WVBJYI3P","short_pith_number":"pith:NZGRZYAV","schema_version":"1.0","canonical_sha256":"6e4d1ce0157e6098f8f47f6d50a708dbee17c3850876f2e8101f3dc0fb8f5217","source":{"kind":"arxiv","id":"2308.13759","version":1},"attestation_state":"computed","paper":{"title":"SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Danny Z. Chen, Pengfei Gu, Shuo Wang, Tao Zhou, Ye Wu, Yizhe Zhang","submitted_at":"2023-08-26T04:46:10Z","abstract_excerpt":"The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image segmentation tasks. In contrast, medical image segmentation tasks often rely on domain-specific knowledge (DSK). In this paper, we propose a novel method that combines the segmentation foundation model (i.e., SAM) with domain-specific knowledge for reliable utilization of unlabeled images in building a medical image segmentation model. Our new method is iterative and consists of two main stages: (1) segmentation model t"},"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":"2308.13759","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-26T04:46:10Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5f5116561473958fb8b65bb73baba66c9c72e95aac7d55a2c66e44aa750a72d1","abstract_canon_sha256":"260716d72ac3fdd2456b1302f84fe667c6f22aa052017354f414870d7659186a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:04.442055Z","signature_b64":"AlrMkjJ7vxrXzJuIdVLC+fo8xvRnNIfvzTEefNgI5QQUkQNEooPhvKwxYTkLs05ZavZocvQUMCjh1UC/7KwqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e4d1ce0157e6098f8f47f6d50a708dbee17c3850876f2e8101f3dc0fb8f5217","last_reissued_at":"2026-07-05T06:45:04.441665Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:04.441665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Danny Z. Chen, Pengfei Gu, Shuo Wang, Tao Zhou, Ye Wu, Yizhe Zhang","submitted_at":"2023-08-26T04:46:10Z","abstract_excerpt":"The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image segmentation tasks. In contrast, medical image segmentation tasks often rely on domain-specific knowledge (DSK). In this paper, we propose a novel method that combines the segmentation foundation model (i.e., SAM) with domain-specific knowledge for reliable utilization of unlabeled images in building a medical image segmentation model. Our new method is iterative and consists of two main stages: (1) segmentation model t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13759","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/2308.13759/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":"2308.13759","created_at":"2026-07-05T06:45:04.441721+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.13759v1","created_at":"2026-07-05T06:45:04.441721+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13759","created_at":"2026-07-05T06:45:04.441721+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZGRZYAVPZQJ","created_at":"2026-07-05T06:45:04.441721+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZGRZYAVPZQJR6HU","created_at":"2026-07-05T06:45:04.441721+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZGRZYAV","created_at":"2026-07-05T06:45:04.441721+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.12602","citing_title":"SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P","json":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P.json","graph_json":"https://pith.science/api/pith-number/NZGRZYAVPZQJR6HUP5WVBJYI3P/graph.json","events_json":"https://pith.science/api/pith-number/NZGRZYAVPZQJR6HUP5WVBJYI3P/events.json","paper":"https://pith.science/paper/NZGRZYAV"},"agent_actions":{"view_html":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P","download_json":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P.json","view_paper":"https://pith.science/paper/NZGRZYAV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.13759&json=true","fetch_graph":"https://pith.science/api/pith-number/NZGRZYAVPZQJR6HUP5WVBJYI3P/graph.json","fetch_events":"https://pith.science/api/pith-number/NZGRZYAVPZQJR6HUP5WVBJYI3P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P/action/storage_attestation","attest_author":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P/action/author_attestation","sign_citation":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P/action/citation_signature","submit_replication":"https://pith.science/pith/NZGRZYAVPZQJR6HUP5WVBJYI3P/action/replication_record"}},"created_at":"2026-07-05T06:45:04.441721+00:00","updated_at":"2026-07-05T06:45:04.441721+00:00"}