{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FFQKJNCHOBUPATHBUL7FB47W3H","short_pith_number":"pith:FFQKJNCH","schema_version":"1.0","canonical_sha256":"2960a4b4477068f04ce1a2fe50f3f6d9c6afd3e65ae995d50643611ef1abfd0e","source":{"kind":"arxiv","id":"2507.18424","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Bulpitt, Edward Ellis, Michael F Byrne, Nasim Parsa, Robert Mendel, Sharib Ali","submitted_at":"2025-07-24T14:01:02Z","abstract_excerpt":"Acquiring and annotating large datasets in ultrasound imaging is challenging due to low contrast, high noise, and susceptibility to artefacts. This process requires significant time and clinical expertise. Self-supervised learning (SSL) offers a promising solution by leveraging unlabelled data to learn useful representations, enabling improved segmentation performance when annotated data is limited. Recent state-of-the-art developments in SSL for video data include V-JEPA, a framework solely based on feature prediction, avoiding pixel level reconstruction or negative samples. We hypothesise th"},"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":"2507.18424","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-24T14:01:02Z","cross_cats_sorted":[],"title_canon_sha256":"5b9903b06947034a18d82f8a716fd41e0a1cc050c9e971899323c39c380124d6","abstract_canon_sha256":"5815eaa38fff3835b95bbfe7ab3550b9e47d04be4fb506164256b478d6c60d56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:47.755716Z","signature_b64":"oL9l8poiWqDXAKvklcFi45ZlfZoEys5VIS1f4DRcn+L43QTsM60zqo8mNlk9l5s4Zvlb0x30IvxHno39+JLpBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2960a4b4477068f04ce1a2fe50f3f6d9c6afd3e65ae995d50643611ef1abfd0e","last_reissued_at":"2026-07-05T11:42:47.755257Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:47.755257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Bulpitt, Edward Ellis, Michael F Byrne, Nasim Parsa, Robert Mendel, Sharib Ali","submitted_at":"2025-07-24T14:01:02Z","abstract_excerpt":"Acquiring and annotating large datasets in ultrasound imaging is challenging due to low contrast, high noise, and susceptibility to artefacts. This process requires significant time and clinical expertise. Self-supervised learning (SSL) offers a promising solution by leveraging unlabelled data to learn useful representations, enabling improved segmentation performance when annotated data is limited. Recent state-of-the-art developments in SSL for video data include V-JEPA, a framework solely based on feature prediction, avoiding pixel level reconstruction or negative samples. We hypothesise th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18424","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/2507.18424/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":"2507.18424","created_at":"2026-07-05T11:42:47.755312+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.18424v1","created_at":"2026-07-05T11:42:47.755312+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18424","created_at":"2026-07-05T11:42:47.755312+00:00"},{"alias_kind":"pith_short_12","alias_value":"FFQKJNCHOBUP","created_at":"2026-07-05T11:42:47.755312+00:00"},{"alias_kind":"pith_short_16","alias_value":"FFQKJNCHOBUPATHB","created_at":"2026-07-05T11:42:47.755312+00:00"},{"alias_kind":"pith_short_8","alias_value":"FFQKJNCH","created_at":"2026-07-05T11:42:47.755312+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/FFQKJNCHOBUPATHBUL7FB47W3H","json":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H.json","graph_json":"https://pith.science/api/pith-number/FFQKJNCHOBUPATHBUL7FB47W3H/graph.json","events_json":"https://pith.science/api/pith-number/FFQKJNCHOBUPATHBUL7FB47W3H/events.json","paper":"https://pith.science/paper/FFQKJNCH"},"agent_actions":{"view_html":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H","download_json":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H.json","view_paper":"https://pith.science/paper/FFQKJNCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.18424&json=true","fetch_graph":"https://pith.science/api/pith-number/FFQKJNCHOBUPATHBUL7FB47W3H/graph.json","fetch_events":"https://pith.science/api/pith-number/FFQKJNCHOBUPATHBUL7FB47W3H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H/action/storage_attestation","attest_author":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H/action/author_attestation","sign_citation":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H/action/citation_signature","submit_replication":"https://pith.science/pith/FFQKJNCHOBUPATHBUL7FB47W3H/action/replication_record"}},"created_at":"2026-07-05T11:42:47.755312+00:00","updated_at":"2026-07-05T11:42:47.755312+00:00"}