{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YN7ISEQG3GKRIT34MNKII77GXL","short_pith_number":"pith:YN7ISEQG","schema_version":"1.0","canonical_sha256":"c37e891206d995144f7c6354847fe6baf855b3b95ef0f9ad73b4a2715a1ae586","source":{"kind":"arxiv","id":"2507.15035","version":1},"attestation_state":"computed","paper":{"title":"OpenBreastUS: Benchmarking Neural Operators for Wave Imaging Using Breast Ultrasound Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Hu, He Sun, Jinzhuo Wang, Linfeng Zhang, Xinliang Liu, Yihang Zheng, Youjia Zheng, Yubing Li, Zeyuan Dong, Zhijun Zeng, Zuoqiang Shi","submitted_at":"2025-07-20T16:36:24Z","abstract_excerpt":"Accurate and efficient simulation of wave equations is crucial in computational wave imaging applications, such as ultrasound computed tomography (USCT), which reconstructs tissue material properties from observed scattered waves. Traditional numerical solvers for wave equations are computationally intensive and often unstable, limiting their practical applications for quasi-real-time image reconstruction. Neural operators offer an innovative approach by accelerating PDE solving using neural networks; however, their effectiveness in realistic imaging is limited because existing datasets oversi"},"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":"2507.15035","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-20T16:36:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ff93fc321e7653db0e5ae2b2992e9d6e004696d5d234ae0a9e86dc8fbcb31393","abstract_canon_sha256":"9821f5fa7794e9385565bec13524ec5b76d95f2b468e1fa5dd716d53ce02a208"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:17.287837Z","signature_b64":"4jeafbZALJ9vGqnoFb1A96NrnArs/RVHSu2BsnlTbmDoOVfxDBm2EjVVB2DVicmFCuoC3x+KelSsnBq1P5AbAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c37e891206d995144f7c6354847fe6baf855b3b95ef0f9ad73b4a2715a1ae586","last_reissued_at":"2026-07-05T11:40:17.287345Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:17.287345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenBreastUS: Benchmarking Neural Operators for Wave Imaging Using Breast Ultrasound Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Hu, He Sun, Jinzhuo Wang, Linfeng Zhang, Xinliang Liu, Yihang Zheng, Youjia Zheng, Yubing Li, Zeyuan Dong, Zhijun Zeng, Zuoqiang Shi","submitted_at":"2025-07-20T16:36:24Z","abstract_excerpt":"Accurate and efficient simulation of wave equations is crucial in computational wave imaging applications, such as ultrasound computed tomography (USCT), which reconstructs tissue material properties from observed scattered waves. Traditional numerical solvers for wave equations are computationally intensive and often unstable, limiting their practical applications for quasi-real-time image reconstruction. Neural operators offer an innovative approach by accelerating PDE solving using neural networks; however, their effectiveness in realistic imaging is limited because existing datasets oversi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.15035","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/2507.15035/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":"2507.15035","created_at":"2026-07-05T11:40:17.287404+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.15035v1","created_at":"2026-07-05T11:40:17.287404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.15035","created_at":"2026-07-05T11:40:17.287404+00:00"},{"alias_kind":"pith_short_12","alias_value":"YN7ISEQG3GKR","created_at":"2026-07-05T11:40:17.287404+00:00"},{"alias_kind":"pith_short_16","alias_value":"YN7ISEQG3GKRIT34","created_at":"2026-07-05T11:40:17.287404+00:00"},{"alias_kind":"pith_short_8","alias_value":"YN7ISEQG","created_at":"2026-07-05T11:40:17.287404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30495","citing_title":"McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation","ref_index":68,"is_internal_anchor":false},{"citing_arxiv_id":"2606.18305","citing_title":"Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30495","citing_title":"McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation","ref_index":68,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL","json":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL.json","graph_json":"https://pith.science/api/pith-number/YN7ISEQG3GKRIT34MNKII77GXL/graph.json","events_json":"https://pith.science/api/pith-number/YN7ISEQG3GKRIT34MNKII77GXL/events.json","paper":"https://pith.science/paper/YN7ISEQG"},"agent_actions":{"view_html":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL","download_json":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL.json","view_paper":"https://pith.science/paper/YN7ISEQG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.15035&json=true","fetch_graph":"https://pith.science/api/pith-number/YN7ISEQG3GKRIT34MNKII77GXL/graph.json","fetch_events":"https://pith.science/api/pith-number/YN7ISEQG3GKRIT34MNKII77GXL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL/action/storage_attestation","attest_author":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL/action/author_attestation","sign_citation":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL/action/citation_signature","submit_replication":"https://pith.science/pith/YN7ISEQG3GKRIT34MNKII77GXL/action/replication_record"}},"created_at":"2026-07-05T11:40:17.287404+00:00","updated_at":"2026-07-05T11:40:17.287404+00:00"}