{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SEASBCYEHPMQ7O3UW26DE43DBK","short_pith_number":"pith:SEASBCYE","schema_version":"1.0","canonical_sha256":"9101208b043bd90fbb74b6bc3273630a82cfb261073d844356d8473e2adeaadd","source":{"kind":"arxiv","id":"2507.00832","version":1},"attestation_state":"computed","paper":{"title":"Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alberto Ceballos-Arroyo, Chu-Hsuan Lin, Geoffrey S Young, Huaizu Jiang, Jisoo Kim, Lei Qin, Ping Liu, Qi Wan, Shrikanth Yadav","submitted_at":"2025-07-01T15:03:43Z","abstract_excerpt":"Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CNN-TR), were trained with 1,186 open-source CTAs (1,373 annotated aneurysms), and evaluated with 143 h"},"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":"2507.00832","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-07-01T15:03:43Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"707eca54ad433a2d91315ec795025962769d7fff80d685660baea696ea0f273e","abstract_canon_sha256":"7b56264da2f8f46ff08e64a90b5ad155b8e5b6cc93a5bc1d61bfa5856bc91d7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:15.395131Z","signature_b64":"2xABCDLvf1KIuqEi10Tn+XfW3nAzvo60Kx6xkh7nbdFaoEYxv7dkazVCsKomdUYfu5VJTWlbEhqyFrHp/w6wCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9101208b043bd90fbb74b6bc3273630a82cfb261073d844356d8473e2adeaadd","last_reissued_at":"2026-07-05T11:30:15.394627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:15.394627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alberto Ceballos-Arroyo, Chu-Hsuan Lin, Geoffrey S Young, Huaizu Jiang, Jisoo Kim, Lei Qin, Ping Liu, Qi Wan, Shrikanth Yadav","submitted_at":"2025-07-01T15:03:43Z","abstract_excerpt":"Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CNN-TR), were trained with 1,186 open-source CTAs (1,373 annotated aneurysms), and evaluated with 143 h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.00832","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/2507.00832/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":"2507.00832","created_at":"2026-07-05T11:30:15.394685+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.00832v1","created_at":"2026-07-05T11:30:15.394685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.00832","created_at":"2026-07-05T11:30:15.394685+00:00"},{"alias_kind":"pith_short_12","alias_value":"SEASBCYEHPMQ","created_at":"2026-07-05T11:30:15.394685+00:00"},{"alias_kind":"pith_short_16","alias_value":"SEASBCYEHPMQ7O3U","created_at":"2026-07-05T11:30:15.394685+00:00"},{"alias_kind":"pith_short_8","alias_value":"SEASBCYE","created_at":"2026-07-05T11:30:15.394685+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/SEASBCYEHPMQ7O3UW26DE43DBK","json":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK.json","graph_json":"https://pith.science/api/pith-number/SEASBCYEHPMQ7O3UW26DE43DBK/graph.json","events_json":"https://pith.science/api/pith-number/SEASBCYEHPMQ7O3UW26DE43DBK/events.json","paper":"https://pith.science/paper/SEASBCYE"},"agent_actions":{"view_html":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK","download_json":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK.json","view_paper":"https://pith.science/paper/SEASBCYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.00832&json=true","fetch_graph":"https://pith.science/api/pith-number/SEASBCYEHPMQ7O3UW26DE43DBK/graph.json","fetch_events":"https://pith.science/api/pith-number/SEASBCYEHPMQ7O3UW26DE43DBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK/action/storage_attestation","attest_author":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK/action/author_attestation","sign_citation":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK/action/citation_signature","submit_replication":"https://pith.science/pith/SEASBCYEHPMQ7O3UW26DE43DBK/action/replication_record"}},"created_at":"2026-07-05T11:30:15.394685+00:00","updated_at":"2026-07-05T11:30:15.394685+00:00"}