{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AFW73HKVMH4X7PRDUS6N3PYM7J","short_pith_number":"pith:AFW73HKV","schema_version":"1.0","canonical_sha256":"016dfd9d5561f97fbe23a4bcddbf0cfa4f47ae0faeea30b984a3fa2a9c3d2564","source":{"kind":"arxiv","id":"2606.04365","version":1},"attestation_state":"computed","paper":{"title":"Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenkun Sun, Jiang Bian, Jie Xu, Jinqian Pan, Renjie Liang, Russell Terry, Zhengkang Fan","submitted_at":"2026-06-03T02:28:57Z","abstract_excerpt":"Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \\textbf{LesionDETR}, a DETR-style architecture with size-distance Hun"},"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":"2606.04365","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T02:28:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"061b2e1f6817bfb510d64a1565735107da25196e28c314cab7b306726c848b97","abstract_canon_sha256":"354a97272f7cb05751d785655eff7f4e282f8f4e0ebde50248f2bfbff79d325d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:05.419226Z","signature_b64":"C9HHW+wjXDW+gPSK9PtWDQmWsmJtEgi0jb/R00dwbmS/xgwkehoaCzZTG40Q/WjMUpDhcx1cZlAMWhqWvMV0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"016dfd9d5561f97fbe23a4bcddbf0cfa4f47ae0faeea30b984a3fa2a9c3d2564","last_reissued_at":"2026-06-04T01:09:05.418807Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:05.418807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenkun Sun, Jiang Bian, Jie Xu, Jinqian Pan, Renjie Liang, Russell Terry, Zhengkang Fan","submitted_at":"2026-06-03T02:28:57Z","abstract_excerpt":"Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \\textbf{LesionDETR}, a DETR-style architecture with size-distance Hun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04365","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/2606.04365/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":"2606.04365","created_at":"2026-06-04T01:09:05.418865+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.04365v1","created_at":"2026-06-04T01:09:05.418865+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04365","created_at":"2026-06-04T01:09:05.418865+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFW73HKVMH4X","created_at":"2026-06-04T01:09:05.418865+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFW73HKVMH4X7PRD","created_at":"2026-06-04T01:09:05.418865+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFW73HKV","created_at":"2026-06-04T01:09:05.418865+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/AFW73HKVMH4X7PRDUS6N3PYM7J","json":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J.json","graph_json":"https://pith.science/api/pith-number/AFW73HKVMH4X7PRDUS6N3PYM7J/graph.json","events_json":"https://pith.science/api/pith-number/AFW73HKVMH4X7PRDUS6N3PYM7J/events.json","paper":"https://pith.science/paper/AFW73HKV"},"agent_actions":{"view_html":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J","download_json":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J.json","view_paper":"https://pith.science/paper/AFW73HKV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.04365&json=true","fetch_graph":"https://pith.science/api/pith-number/AFW73HKVMH4X7PRDUS6N3PYM7J/graph.json","fetch_events":"https://pith.science/api/pith-number/AFW73HKVMH4X7PRDUS6N3PYM7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J/action/storage_attestation","attest_author":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J/action/author_attestation","sign_citation":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J/action/citation_signature","submit_replication":"https://pith.science/pith/AFW73HKVMH4X7PRDUS6N3PYM7J/action/replication_record"}},"created_at":"2026-06-04T01:09:05.418865+00:00","updated_at":"2026-06-04T01:09:05.418865+00:00"}