{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2GSVGHGJ2BRJWCIAHXMX3UCATI","short_pith_number":"pith:2GSVGHGJ","schema_version":"1.0","canonical_sha256":"d1a5531cc9d0629b09003dd97dd0409a2b66ef12356bc857cad736669f36b889","source":{"kind":"arxiv","id":"2605.28397","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Alzheimer's Prediction","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alicia Troncoso Lora, Alireza Moayedikia, Sara Fin, Uffe Kock Wiil","submitted_at":"2026-05-27T12:33:46Z","abstract_excerpt":"Predicting conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) is critical for early intervention. Current deep learning paradigms predominantly rely on cross-sectional structural MRI, neglecting prognostic value in patient-specific anatomical trajectories. We introduce the Temporal Adaptive Fusion Network (TAF-Net), a hybrid CNN-Transformer architecture that models paired longitudinal 3D MRI scans. Central to TAF-Net is a Temporal Fusion Module governed by an Adaptive Temporal Gate, which learns patient-specific weightings to synthesize three spatiotemporal representat"},"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":"2605.28397","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T12:33:46Z","cross_cats_sorted":[],"title_canon_sha256":"c36848726e5c0d1844b046b5df231b20f1c228550890d6a09f1748fe0f7a4a3c","abstract_canon_sha256":"77c4e63ce50dd110d62b2798a3b8f3f3a9181f8e33499141f84dc38a5eff6e7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:05:17.036936Z","signature_b64":"yW8L3YmzU4ujrzMHLvFGzWopvt7Gy5snjb9nqcOnv/hxFpwOtxn2Q2ZI1iEKk31wCk+i1JoO8Ex4bSFxxRjIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1a5531cc9d0629b09003dd97dd0409a2b66ef12356bc857cad736669f36b889","last_reissued_at":"2026-05-28T01:05:17.036512Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:05:17.036512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Alzheimer's Prediction","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alicia Troncoso Lora, Alireza Moayedikia, Sara Fin, Uffe Kock Wiil","submitted_at":"2026-05-27T12:33:46Z","abstract_excerpt":"Predicting conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) is critical for early intervention. Current deep learning paradigms predominantly rely on cross-sectional structural MRI, neglecting prognostic value in patient-specific anatomical trajectories. We introduce the Temporal Adaptive Fusion Network (TAF-Net), a hybrid CNN-Transformer architecture that models paired longitudinal 3D MRI scans. Central to TAF-Net is a Temporal Fusion Module governed by an Adaptive Temporal Gate, which learns patient-specific weightings to synthesize three spatiotemporal representat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28397","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/2605.28397/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":"2605.28397","created_at":"2026-05-28T01:05:17.036571+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28397v1","created_at":"2026-05-28T01:05:17.036571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28397","created_at":"2026-05-28T01:05:17.036571+00:00"},{"alias_kind":"pith_short_12","alias_value":"2GSVGHGJ2BRJ","created_at":"2026-05-28T01:05:17.036571+00:00"},{"alias_kind":"pith_short_16","alias_value":"2GSVGHGJ2BRJWCIA","created_at":"2026-05-28T01:05:17.036571+00:00"},{"alias_kind":"pith_short_8","alias_value":"2GSVGHGJ","created_at":"2026-05-28T01:05:17.036571+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/2GSVGHGJ2BRJWCIAHXMX3UCATI","json":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI.json","graph_json":"https://pith.science/api/pith-number/2GSVGHGJ2BRJWCIAHXMX3UCATI/graph.json","events_json":"https://pith.science/api/pith-number/2GSVGHGJ2BRJWCIAHXMX3UCATI/events.json","paper":"https://pith.science/paper/2GSVGHGJ"},"agent_actions":{"view_html":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI","download_json":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI.json","view_paper":"https://pith.science/paper/2GSVGHGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28397&json=true","fetch_graph":"https://pith.science/api/pith-number/2GSVGHGJ2BRJWCIAHXMX3UCATI/graph.json","fetch_events":"https://pith.science/api/pith-number/2GSVGHGJ2BRJWCIAHXMX3UCATI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI/action/storage_attestation","attest_author":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI/action/author_attestation","sign_citation":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI/action/citation_signature","submit_replication":"https://pith.science/pith/2GSVGHGJ2BRJWCIAHXMX3UCATI/action/replication_record"}},"created_at":"2026-05-28T01:05:17.036571+00:00","updated_at":"2026-05-28T01:05:17.036571+00:00"}