{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OK7ELLNYQFWGMHFF3WG6UUHRWD","short_pith_number":"pith:OK7ELLNY","schema_version":"1.0","canonical_sha256":"72be45adb8816c661ca5dd8dea50f1b0e5b20386bdde80a780b71896521f8031","source":{"kind":"arxiv","id":"2403.18271","version":1},"attestation_state":"computed","paper":{"title":"Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongru Zhu, Liangqiong Qu, Qingyue Wei, Wei Shao, Yan Wang, Yuyin Zhou, Zhiheng Cheng","submitted_at":"2024-03-27T05:55:16Z","abstract_excerpt":"The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medical imaging presents challenges, requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performance. This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure. In the initial stage, H-SAM employs SAM's original decoder to generate a p"},"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":"2403.18271","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-27T05:55:16Z","cross_cats_sorted":[],"title_canon_sha256":"cf9e5235bf0e1df4264c640eb6b1b02a627cb9234726af73ffad33b227738c6b","abstract_canon_sha256":"a567c6c3a267dc1e2559185f4eea44b018a5cf99924c57a2ff91d0d2e175084f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:07.142963Z","signature_b64":"nqycOkLEpIwSOoPmsII/IK2cQLX8WVpI+EhEF+eYwtageAxaQgVq53F85CG55PNkQ2AAlyR9cxMwH21oghqSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72be45adb8816c661ca5dd8dea50f1b0e5b20386bdde80a780b71896521f8031","last_reissued_at":"2026-07-05T08:01:07.142560Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:07.142560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongru Zhu, Liangqiong Qu, Qingyue Wei, Wei Shao, Yan Wang, Yuyin Zhou, Zhiheng Cheng","submitted_at":"2024-03-27T05:55:16Z","abstract_excerpt":"The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medical imaging presents challenges, requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performance. This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure. In the initial stage, H-SAM employs SAM's original decoder to generate a p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18271","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/2403.18271/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":"2403.18271","created_at":"2026-07-05T08:01:07.142617+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.18271v1","created_at":"2026-07-05T08:01:07.142617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18271","created_at":"2026-07-05T08:01:07.142617+00:00"},{"alias_kind":"pith_short_12","alias_value":"OK7ELLNYQFWG","created_at":"2026-07-05T08:01:07.142617+00:00"},{"alias_kind":"pith_short_16","alias_value":"OK7ELLNYQFWGMHFF","created_at":"2026-07-05T08:01:07.142617+00:00"},{"alias_kind":"pith_short_8","alias_value":"OK7ELLNY","created_at":"2026-07-05T08:01:07.142617+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/OK7ELLNYQFWGMHFF3WG6UUHRWD","json":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD.json","graph_json":"https://pith.science/api/pith-number/OK7ELLNYQFWGMHFF3WG6UUHRWD/graph.json","events_json":"https://pith.science/api/pith-number/OK7ELLNYQFWGMHFF3WG6UUHRWD/events.json","paper":"https://pith.science/paper/OK7ELLNY"},"agent_actions":{"view_html":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD","download_json":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD.json","view_paper":"https://pith.science/paper/OK7ELLNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.18271&json=true","fetch_graph":"https://pith.science/api/pith-number/OK7ELLNYQFWGMHFF3WG6UUHRWD/graph.json","fetch_events":"https://pith.science/api/pith-number/OK7ELLNYQFWGMHFF3WG6UUHRWD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD/action/storage_attestation","attest_author":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD/action/author_attestation","sign_citation":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD/action/citation_signature","submit_replication":"https://pith.science/pith/OK7ELLNYQFWGMHFF3WG6UUHRWD/action/replication_record"}},"created_at":"2026-07-05T08:01:07.142617+00:00","updated_at":"2026-07-05T08:01:07.142617+00:00"}