{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:O7TV3G3VFRERV3W4T4PT5QD22A","short_pith_number":"pith:O7TV3G3V","schema_version":"1.0","canonical_sha256":"77e75d9b752c491aeedc9f1f3ec07ad02b1f63609f86503ef8282474517f79f6","source":{"kind":"arxiv","id":"2505.09274","version":1},"attestation_state":"computed","paper":{"title":"Recent Advances in Medical Imaging Segmentation: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abdenour Hadid, Fares Bougourzi","submitted_at":"2025-05-14T10:48:37Z","abstract_excerpt":"Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achieving robust generalization and domain adaptation remains a significant hurdle, particularly given the resource-intensive nature of some proposed models and their reliance on domain expertise. This survey "},"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":"2505.09274","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T10:48:37Z","cross_cats_sorted":[],"title_canon_sha256":"765be1cfa039bdeb5619bac9032e91df74ac6055aa5e62a8fe53e799215b99c4","abstract_canon_sha256":"816a388eb124a066b2218059f4af21e769f7f27709c6de5e0f7511e178bbf2f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:05.517637Z","signature_b64":"XB211yYyAMNSzELA7OrVSRuJYc15h8kcn4MzCvNtPevsXcCHpTKdjSw0cVRxP02iVCSiA7K/zOn94+cT+dERCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77e75d9b752c491aeedc9f1f3ec07ad02b1f63609f86503ef8282474517f79f6","last_reissued_at":"2026-07-05T11:03:05.517134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:05.517134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recent Advances in Medical Imaging Segmentation: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abdenour Hadid, Fares Bougourzi","submitted_at":"2025-05-14T10:48:37Z","abstract_excerpt":"Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achieving robust generalization and domain adaptation remains a significant hurdle, particularly given the resource-intensive nature of some proposed models and their reliance on domain expertise. This survey "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09274","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/2505.09274/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":"2505.09274","created_at":"2026-07-05T11:03:05.517193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.09274v1","created_at":"2026-07-05T11:03:05.517193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09274","created_at":"2026-07-05T11:03:05.517193+00:00"},{"alias_kind":"pith_short_12","alias_value":"O7TV3G3VFRER","created_at":"2026-07-05T11:03:05.517193+00:00"},{"alias_kind":"pith_short_16","alias_value":"O7TV3G3VFRERV3W4","created_at":"2026-07-05T11:03:05.517193+00:00"},{"alias_kind":"pith_short_8","alias_value":"O7TV3G3V","created_at":"2026-07-05T11:03:05.517193+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20436","citing_title":"Lighting-aware Unified Model for Instance Segmentation","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20436","citing_title":"Lighting-aware Unified Model for Instance Segmentation","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19206","citing_title":"When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A","json":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A.json","graph_json":"https://pith.science/api/pith-number/O7TV3G3VFRERV3W4T4PT5QD22A/graph.json","events_json":"https://pith.science/api/pith-number/O7TV3G3VFRERV3W4T4PT5QD22A/events.json","paper":"https://pith.science/paper/O7TV3G3V"},"agent_actions":{"view_html":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A","download_json":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A.json","view_paper":"https://pith.science/paper/O7TV3G3V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.09274&json=true","fetch_graph":"https://pith.science/api/pith-number/O7TV3G3VFRERV3W4T4PT5QD22A/graph.json","fetch_events":"https://pith.science/api/pith-number/O7TV3G3VFRERV3W4T4PT5QD22A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A/action/storage_attestation","attest_author":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A/action/author_attestation","sign_citation":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A/action/citation_signature","submit_replication":"https://pith.science/pith/O7TV3G3VFRERV3W4T4PT5QD22A/action/replication_record"}},"created_at":"2026-07-05T11:03:05.517193+00:00","updated_at":"2026-07-05T11:03:05.517193+00:00"}