{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:C6KLUDR5CO4OM6F3PLQ5II7R7N","short_pith_number":"pith:C6KLUDR5","schema_version":"1.0","canonical_sha256":"1794ba0e3d13b8e678bb7ae1d423f1fb4309624c885f1f2f9ee0f5349e22a4b4","source":{"kind":"arxiv","id":"2605.13688","version":1},"attestation_state":"computed","paper":{"title":"MedCore: Boundary-Preserving Medical Core Pruning for MedSAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MedCore prunes MedSAM parameters by 60 percent while preserving boundary fidelity through dual-intervention scoring and logit-level boundary leverage.","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cenwei Zhang, Lei You, Suncheng Xiang","submitted_at":"2026-05-13T15:42:39Z","abstract_excerpt":"Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large for many clinical settings. Compression is also risky in medicine because a model can keep high Dice while losing boundary fidelity. We propose MedCore, a structured pruning framework for MedSAM. The main idea is to preserve two kinds of structures: structures that became important during SAM-to-MedSAM adaptation, and structures that have high boundary leverage. We identify the first type by a dual-intervention score that compares zeroing a group"},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.13688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T15:42:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0472c7bd8180cb54a5926cd75daeadbdd35c099171c76937a508ebd0d8e89c66","abstract_canon_sha256":"7a6682292904ed1d24a1dc5849ae175959d684a1310296e75eef6cc0ff3d56af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:16.976366Z","signature_b64":"J48+eXOuE0XHPLEz7706D0XUpjElKwA4kvXUQXm1OymVyYcVLu9ixor6S+2x2uKMO/HV4EGbDSllR4hTV6NcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1794ba0e3d13b8e678bb7ae1d423f1fb4309624c885f1f2f9ee0f5349e22a4b4","last_reissued_at":"2026-05-18T02:44:16.975916Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:16.975916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedCore: Boundary-Preserving Medical Core Pruning for MedSAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MedCore prunes MedSAM parameters by 60 percent while preserving boundary fidelity through dual-intervention scoring and logit-level boundary leverage.","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cenwei Zhang, Lei You, Suncheng Xiang","submitted_at":"2026-05-13T15:42:39Z","abstract_excerpt":"Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large for many clinical settings. Compression is also risky in medicine because a model can keep high Dice while losing boundary fidelity. We propose MedCore, a structured pruning framework for MedSAM. The main idea is to preserve two kinds of structures: structures that became important during SAM-to-MedSAM adaptation, and structures that have high boundary leverage. We identify the first type by a dual-intervention score that compares zeroing a group"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"On polyp segmentation benchmarks, MedCore reduces parameters by 60.0% and FLOPs by 58.4% while achieving Dice 0.9549, Boundary F1 0.6388, and HD95 5.14 after recovery fine-tuning. It also reaches 86.6% parameter reduction with strong boundary quality.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the dual-intervention score and boundary-aware Fisher estimation correctly identify preservable structures without hidden degradation, and that the boundary leverage principle accurately predicts and controls compression-induced boundary displacement in practice.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MedCore prunes MedSAM parameters by 60 percent while preserving boundary fidelity through dual-intervention scoring and logit-level boundary leverage.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ffdf49ef323b6afecf8789f0d18464be073ce307234611eb26e523f00fbb9fda"},"source":{"id":"2605.13688","kind":"arxiv","version":1},"verdict":{"id":"c52ce991-7801-4513-b82a-cf5192dfed92","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T20:09:43.803151Z","strongest_claim":"On polyp segmentation benchmarks, MedCore reduces parameters by 60.0% and FLOPs by 58.4% while achieving Dice 0.9549, Boundary F1 0.6388, and HD95 5.14 after recovery fine-tuning. It also reaches 86.6% parameter reduction with strong boundary quality.","one_line_summary":"MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the dual-intervention score and boundary-aware Fisher estimation correctly identify preservable structures without hidden degradation, and that the boundary leverage principle accurately predicts and controls compression-induced boundary displacement in practice.","pith_extraction_headline":"MedCore prunes MedSAM parameters by 60 percent while preserving boundary fidelity through dual-intervention scoring and logit-level boundary leverage."},"references":{"count":51,"sample":[{"doi":"","year":2023,"title":"Segment Anything","work_id":"2bbf46ca-720a-45a1-8e9c-10c33fbeada0","ref_index":1,"cited_arxiv_id":"2304.02643","is_internal_anchor":true},{"doi":"","year":2024,"title":"Segment anything in medical images.Nature Communications, 15:654, 2024","work_id":"27d74308-606e-450f-9c86-8f919bfe8573","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","ref_index":3,"cited_arxiv_id":"2010.11929","is_internal_anchor":true},{"doi":"","year":2021,"title":"An image is worth 16x16 words: Transformers for image recognition at scale","work_id":"b2cc82a1-974a-4867-8a88-379d15de9985","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Customized segment anything model for medical image segmentation, 2023","work_id":"472dad9d-9f7b-4c0e-8d78-095fc2ed9ae2","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":51,"snapshot_sha256":"5833864fd9efc22beeea4684c6c55cf8d7be8ab812c0ccdc7f4d2be8c8174791","internal_anchors":5},"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":"2605.13688","created_at":"2026-05-18T02:44:16.975984+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.13688v1","created_at":"2026-05-18T02:44:16.975984+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13688","created_at":"2026-05-18T02:44:16.975984+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6KLUDR5CO4O","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6KLUDR5CO4OM6F3","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6KLUDR5","created_at":"2026-05-18T12:33:37.589309+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/C6KLUDR5CO4OM6F3PLQ5II7R7N","json":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N.json","graph_json":"https://pith.science/api/pith-number/C6KLUDR5CO4OM6F3PLQ5II7R7N/graph.json","events_json":"https://pith.science/api/pith-number/C6KLUDR5CO4OM6F3PLQ5II7R7N/events.json","paper":"https://pith.science/paper/C6KLUDR5"},"agent_actions":{"view_html":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N","download_json":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N.json","view_paper":"https://pith.science/paper/C6KLUDR5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.13688&json=true","fetch_graph":"https://pith.science/api/pith-number/C6KLUDR5CO4OM6F3PLQ5II7R7N/graph.json","fetch_events":"https://pith.science/api/pith-number/C6KLUDR5CO4OM6F3PLQ5II7R7N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N/action/storage_attestation","attest_author":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N/action/author_attestation","sign_citation":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N/action/citation_signature","submit_replication":"https://pith.science/pith/C6KLUDR5CO4OM6F3PLQ5II7R7N/action/replication_record"}},"created_at":"2026-05-18T02:44:16.975984+00:00","updated_at":"2026-05-18T02:44:16.975984+00:00"}