{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:D2SMCNWZ2TGDWRVSBXLAWLVBKV","short_pith_number":"pith:D2SMCNWZ","schema_version":"1.0","canonical_sha256":"1ea4c136d9d4cc3b46b20dd60b2ea1557c0c7fadec02f37ab919b8263fc2f87f","source":{"kind":"arxiv","id":"2404.16325","version":1},"attestation_state":"computed","paper":{"title":"Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Angeles M. Perez-Agosto, Carmit Shiran, Doron Shaked, Elay Dahan, Hedda Cohen Indelman, Nati Daniel","submitted_at":"2024-04-25T04:21:57Z","abstract_excerpt":"Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision. Further, the significant domain gap between natural and medical images in general and ultrasound images in particular hinders fine-tuning models trained on natural images to the task at hand. In this work, we address the performance degradation of segmentation models in low-data regimes and propose a prompt-less segmentation method harnessing the ability of segmentation foundation models to segment abstra"},"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":"2404.16325","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-04-25T04:21:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"39581209b7eb55ca137fda248660115d38798aa9ab01dbe4f94b14c6c3edb93a","abstract_canon_sha256":"8a0e9d2b3ba08e873c6efda9f0ba6326d1b4fd4d948706c6527731a5f9e855d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:04.696612Z","signature_b64":"vKWX/80qVVqTxmNfZJpM77kMS6+jBQf8lNCuwb5nIGMO0Hy0OTXLYqUH1bR9YIHVwz6hzbDTXuiJhhlGK4wMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ea4c136d9d4cc3b46b20dd60b2ea1557c0c7fadec02f37ab919b8263fc2f87f","last_reissued_at":"2026-07-05T08:12:04.696020Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:04.696020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Angeles M. Perez-Agosto, Carmit Shiran, Doron Shaked, Elay Dahan, Hedda Cohen Indelman, Nati Daniel","submitted_at":"2024-04-25T04:21:57Z","abstract_excerpt":"Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision. Further, the significant domain gap between natural and medical images in general and ultrasound images in particular hinders fine-tuning models trained on natural images to the task at hand. In this work, we address the performance degradation of segmentation models in low-data regimes and propose a prompt-less segmentation method harnessing the ability of segmentation foundation models to segment abstra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16325","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/2404.16325/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":"2404.16325","created_at":"2026-07-05T08:12:04.696076+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16325v1","created_at":"2026-07-05T08:12:04.696076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16325","created_at":"2026-07-05T08:12:04.696076+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2SMCNWZ2TGD","created_at":"2026-07-05T08:12:04.696076+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2SMCNWZ2TGDWRVS","created_at":"2026-07-05T08:12:04.696076+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2SMCNWZ","created_at":"2026-07-05T08:12:04.696076+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.05833","citing_title":"CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV","json":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV.json","graph_json":"https://pith.science/api/pith-number/D2SMCNWZ2TGDWRVSBXLAWLVBKV/graph.json","events_json":"https://pith.science/api/pith-number/D2SMCNWZ2TGDWRVSBXLAWLVBKV/events.json","paper":"https://pith.science/paper/D2SMCNWZ"},"agent_actions":{"view_html":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV","download_json":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV.json","view_paper":"https://pith.science/paper/D2SMCNWZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16325&json=true","fetch_graph":"https://pith.science/api/pith-number/D2SMCNWZ2TGDWRVSBXLAWLVBKV/graph.json","fetch_events":"https://pith.science/api/pith-number/D2SMCNWZ2TGDWRVSBXLAWLVBKV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV/action/storage_attestation","attest_author":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV/action/author_attestation","sign_citation":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV/action/citation_signature","submit_replication":"https://pith.science/pith/D2SMCNWZ2TGDWRVSBXLAWLVBKV/action/replication_record"}},"created_at":"2026-07-05T08:12:04.696076+00:00","updated_at":"2026-07-05T08:12:04.696076+00:00"}