{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GDOWQZTALAI4SNJEAUWSR4ALIN","short_pith_number":"pith:GDOWQZTA","schema_version":"1.0","canonical_sha256":"30dd6866605811c93524052d28f00b43748e38ec6777ecc54a1b947e5d5a8644","source":{"kind":"arxiv","id":"2503.17475","version":1},"attestation_state":"computed","paper":{"title":"Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alvin Chen, Balasundar Raju, Bryson Hicks, Christopher Moore, Cristiana Baloescu, Cynthia Gregory, David O. Kessler, Gary Y. Li, Jeffrey Shupp, Jochen Kruecker, Kenton Gregory, Li Chen, Maria Parker, Nikolai Schnittke","submitted_at":"2025-03-21T18:39:42Z","abstract_excerpt":"Computer-aided pathology detection algorithms for video-based imaging modalities must accurately interpret complex spatiotemporal information by integrating findings across multiple frames. Current state-of-the-art methods operate by classifying on video sub-volumes (tubelets), but they often lose global spatial context by focusing only on local regions within detection ROIs. Here we propose a lightweight framework for tubelet-based object detection and video classification that preserves both global spatial context and fine spatiotemporal features. To address the loss of global context, we em"},"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":"2503.17475","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-21T18:39:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8ed6206db6ca22cbe874b72556ce2ad0663924909e71d6ecc8987b8714e458aa","abstract_canon_sha256":"882957c67018218446fb3999742338f5e8b626ea17c3295210cae3ba031beb19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:10.154819Z","signature_b64":"FoWPp6V7LA33nC0bamPgDksF3YDrsZG1+GqiCNcpM5E47uGwBfWwkXeWaY88+XWwvzfLGBGI46UWMBo9B+E0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30dd6866605811c93524052d28f00b43748e38ec6777ecc54a1b947e5d5a8644","last_reissued_at":"2026-07-05T10:37:10.154178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:10.154178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alvin Chen, Balasundar Raju, Bryson Hicks, Christopher Moore, Cristiana Baloescu, Cynthia Gregory, David O. Kessler, Gary Y. Li, Jeffrey Shupp, Jochen Kruecker, Kenton Gregory, Li Chen, Maria Parker, Nikolai Schnittke","submitted_at":"2025-03-21T18:39:42Z","abstract_excerpt":"Computer-aided pathology detection algorithms for video-based imaging modalities must accurately interpret complex spatiotemporal information by integrating findings across multiple frames. Current state-of-the-art methods operate by classifying on video sub-volumes (tubelets), but they often lose global spatial context by focusing only on local regions within detection ROIs. Here we propose a lightweight framework for tubelet-based object detection and video classification that preserves both global spatial context and fine spatiotemporal features. To address the loss of global context, we em"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17475","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/2503.17475/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":"2503.17475","created_at":"2026-07-05T10:37:10.154253+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17475v1","created_at":"2026-07-05T10:37:10.154253+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17475","created_at":"2026-07-05T10:37:10.154253+00:00"},{"alias_kind":"pith_short_12","alias_value":"GDOWQZTALAI4","created_at":"2026-07-05T10:37:10.154253+00:00"},{"alias_kind":"pith_short_16","alias_value":"GDOWQZTALAI4SNJE","created_at":"2026-07-05T10:37:10.154253+00:00"},{"alias_kind":"pith_short_8","alias_value":"GDOWQZTA","created_at":"2026-07-05T10:37:10.154253+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/GDOWQZTALAI4SNJEAUWSR4ALIN","json":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN.json","graph_json":"https://pith.science/api/pith-number/GDOWQZTALAI4SNJEAUWSR4ALIN/graph.json","events_json":"https://pith.science/api/pith-number/GDOWQZTALAI4SNJEAUWSR4ALIN/events.json","paper":"https://pith.science/paper/GDOWQZTA"},"agent_actions":{"view_html":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN","download_json":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN.json","view_paper":"https://pith.science/paper/GDOWQZTA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17475&json=true","fetch_graph":"https://pith.science/api/pith-number/GDOWQZTALAI4SNJEAUWSR4ALIN/graph.json","fetch_events":"https://pith.science/api/pith-number/GDOWQZTALAI4SNJEAUWSR4ALIN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN/action/storage_attestation","attest_author":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN/action/author_attestation","sign_citation":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN/action/citation_signature","submit_replication":"https://pith.science/pith/GDOWQZTALAI4SNJEAUWSR4ALIN/action/replication_record"}},"created_at":"2026-07-05T10:37:10.154253+00:00","updated_at":"2026-07-05T10:37:10.154253+00:00"}