{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GZFNHWRMF3BNEYFIEWKPTEOWKO","short_pith_number":"pith:GZFNHWRM","schema_version":"1.0","canonical_sha256":"364ad3da2c2ec2d260a82594f991d653b6f1f077333e0ac68b127eadd1cfd977","source":{"kind":"arxiv","id":"2501.16400","version":1},"attestation_state":"computed","paper":{"title":"CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Ahmed Elazab, Changmiao Wang, Guanyu Zhou, Ke Zhuang, Ruiquan Ge, Xiaopeng Fan, Xiao Yu, Yin Shen, Yuan Tian, Yucheng Zhao, Zhaojie Fang","submitted_at":"2025-01-27T06:40:45Z","abstract_excerpt":"Pulmonary nodules are an early sign of lung cancer, and detecting them early is vital for improving patient survival rates. Most current methods use only single Computed Tomography (CT) images to assess nodule malignancy. However, doctors typically make a comprehensive assessment in clinical practice by integrating follow-up CT scans with clinical data. To enhance this process, our study introduces a Cross-Modal Spatiotemporal Fusion Network, named CSF-Net, designed to predict the malignancy of pulmonary nodules using follow-up CT scans. This approach simulates the decision-making process of c"},"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":"2501.16400","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-01-27T06:40:45Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"cd6b8b4dee24bee32f6c497ca9b732e492bb9122435cbf4c6f4d8f673ee4f174","abstract_canon_sha256":"e8dd76da6a1b4b428d9348707d1c9384ee022493a188488e66fb8d85a0ce9a1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:33.274422Z","signature_b64":"ixDntsl2ctdVvKabtoFCckwJGCkL/sOssAE+NpgtzHty1ALZSaavAnSJM9b5mcjojKRz0CfpeNM+mLYajibUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"364ad3da2c2ec2d260a82594f991d653b6f1f077333e0ac68b127eadd1cfd977","last_reissued_at":"2026-07-05T10:06:33.273934Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:33.273934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Ahmed Elazab, Changmiao Wang, Guanyu Zhou, Ke Zhuang, Ruiquan Ge, Xiaopeng Fan, Xiao Yu, Yin Shen, Yuan Tian, Yucheng Zhao, Zhaojie Fang","submitted_at":"2025-01-27T06:40:45Z","abstract_excerpt":"Pulmonary nodules are an early sign of lung cancer, and detecting them early is vital for improving patient survival rates. Most current methods use only single Computed Tomography (CT) images to assess nodule malignancy. However, doctors typically make a comprehensive assessment in clinical practice by integrating follow-up CT scans with clinical data. To enhance this process, our study introduces a Cross-Modal Spatiotemporal Fusion Network, named CSF-Net, designed to predict the malignancy of pulmonary nodules using follow-up CT scans. This approach simulates the decision-making process of c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16400","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/2501.16400/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":"2501.16400","created_at":"2026-07-05T10:06:33.273991+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16400v1","created_at":"2026-07-05T10:06:33.273991+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16400","created_at":"2026-07-05T10:06:33.273991+00:00"},{"alias_kind":"pith_short_12","alias_value":"GZFNHWRMF3BN","created_at":"2026-07-05T10:06:33.273991+00:00"},{"alias_kind":"pith_short_16","alias_value":"GZFNHWRMF3BNEYFI","created_at":"2026-07-05T10:06:33.273991+00:00"},{"alias_kind":"pith_short_8","alias_value":"GZFNHWRM","created_at":"2026-07-05T10:06:33.273991+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.16400","citing_title":"CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO","json":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO.json","graph_json":"https://pith.science/api/pith-number/GZFNHWRMF3BNEYFIEWKPTEOWKO/graph.json","events_json":"https://pith.science/api/pith-number/GZFNHWRMF3BNEYFIEWKPTEOWKO/events.json","paper":"https://pith.science/paper/GZFNHWRM"},"agent_actions":{"view_html":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO","download_json":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO.json","view_paper":"https://pith.science/paper/GZFNHWRM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16400&json=true","fetch_graph":"https://pith.science/api/pith-number/GZFNHWRMF3BNEYFIEWKPTEOWKO/graph.json","fetch_events":"https://pith.science/api/pith-number/GZFNHWRMF3BNEYFIEWKPTEOWKO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO/action/storage_attestation","attest_author":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO/action/author_attestation","sign_citation":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO/action/citation_signature","submit_replication":"https://pith.science/pith/GZFNHWRMF3BNEYFIEWKPTEOWKO/action/replication_record"}},"created_at":"2026-07-05T10:06:33.273991+00:00","updated_at":"2026-07-05T10:06:33.273991+00:00"}