{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RTJIFOX5MUJPLRPFKGPOQZNH4U","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a8040b63e2a5637fdee545f0dcc1aec7f7fdf927633f1d1bcb49827e817b814d","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-05T19:35:10Z","title_canon_sha256":"c8b43954a8e9ea48542617055ea28d06cc75631e2bfd0b75628d657f343afdc7"},"schema_version":"1.0","source":{"id":"2411.03464","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.03464","created_at":"2026-07-05T09:31:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.03464v1","created_at":"2026-07-05T09:31:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.03464","created_at":"2026-07-05T09:31:33Z"},{"alias_kind":"pith_short_12","alias_value":"RTJIFOX5MUJP","created_at":"2026-07-05T09:31:33Z"},{"alias_kind":"pith_short_16","alias_value":"RTJIFOX5MUJPLRPF","created_at":"2026-07-05T09:31:33Z"},{"alias_kind":"pith_short_8","alias_value":"RTJIFOX5","created_at":"2026-07-05T09:31:33Z"}],"graph_snapshots":[{"event_id":"sha256:bfde66d2678187d069bf603585aaa52f582f42b4caef72c93dbce40636a18191","target":"graph","created_at":"2026-07-05T09:31:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.03464/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular tissue. To address this, we propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and then incorporates these structures into a deep-learning-ba","authors_text":"Chao Chen, Chuan Huang, Fan Wang, Gagandeep Singh, Haibin Ling, Luke Partyka, Nicole Sakla, Nil Rawal, Prateek Prasanna, Wei Zhao, Zhilin Zou","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-05T19:35:10Z","title":"TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.03464","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a09b45e9b0a38dd5bdd1401734d36a426c01b3b7d4ea2cb9b74023aafdd058c1","target":"record","created_at":"2026-07-05T09:31:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a8040b63e2a5637fdee545f0dcc1aec7f7fdf927633f1d1bcb49827e817b814d","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-05T19:35:10Z","title_canon_sha256":"c8b43954a8e9ea48542617055ea28d06cc75631e2bfd0b75628d657f343afdc7"},"schema_version":"1.0","source":{"id":"2411.03464","kind":"arxiv","version":1}},"canonical_sha256":"8cd282bafd6512f5c5e5519ee865a7e50e3aec9a6aa3fa7eaf5269c7654dd3d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8cd282bafd6512f5c5e5519ee865a7e50e3aec9a6aa3fa7eaf5269c7654dd3d1","first_computed_at":"2026-07-05T09:31:33.107865Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:31:33.107865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8NMJtSYON7ibUMsKLVauRHwmCWohCSn1+eFk1LfapQwsjE/Pm8N4RP0unufAtdpdx9z7Z7IMEbO+46Ae8FSXBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:31:33.108361Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.03464","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a09b45e9b0a38dd5bdd1401734d36a426c01b3b7d4ea2cb9b74023aafdd058c1","sha256:bfde66d2678187d069bf603585aaa52f582f42b4caef72c93dbce40636a18191"],"state_sha256":"2e9efc416c0d56f482048a04d44db7db622ce8935b25ece87529aa0b14103fec"}