{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KVUFNZ25BGZCJUSM6WY5O7AO7E","short_pith_number":"pith:KVUFNZ25","schema_version":"1.0","canonical_sha256":"556856e75d09b224d24cf5b1d77c0ef93e6d868e8ac99a7ebdfd290ecee4b309","source":{"kind":"arxiv","id":"2512.09185","version":4},"attestation_state":"computed","paper":{"title":"Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chao Li, Hao Chen, Qi Chen, Rui Yin, Yifan Chen","submitted_at":"2025-12-09T23:13:54Z","abstract_excerpt":"Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity with random denoising process. In this work, we propose to treat the disease dynamic as a velocity field and leverage Flow Matching (FM) to align the temporal evolution of patient data. "},"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":"2512.09185","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-09T23:13:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5e29cadaf73563e4916f5e7f284d06b4a6d6c62982fa04d5347bb9058fbab40a","abstract_canon_sha256":"49d0348e11b47d07ba7fe0c317e8ecd63b14c25f6ed549f6ef43c7415350d5cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:18.219187Z","signature_b64":"c7ypBAcDA7D29CVwYznqd1hKjdMgy94cEfBsJBrDbTFPmxSySm41g+PTfwZTm0Xu7elp30CM2Dapemo09yzcDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"556856e75d09b224d24cf5b1d77c0ef93e6d868e8ac99a7ebdfd290ecee4b309","last_reissued_at":"2026-06-19T16:11:18.218749Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:18.218749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chao Li, Hao Chen, Qi Chen, Rui Yin, Yifan Chen","submitted_at":"2025-12-09T23:13:54Z","abstract_excerpt":"Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity with random denoising process. In this work, we propose to treat the disease dynamic as a velocity field and leverage Flow Matching (FM) to align the temporal evolution of patient data. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.09185","kind":"arxiv","version":4},"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/2512.09185/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":"2512.09185","created_at":"2026-06-19T16:11:18.218806+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.09185v4","created_at":"2026-06-19T16:11:18.218806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.09185","created_at":"2026-06-19T16:11:18.218806+00:00"},{"alias_kind":"pith_short_12","alias_value":"KVUFNZ25BGZC","created_at":"2026-06-19T16:11:18.218806+00:00"},{"alias_kind":"pith_short_16","alias_value":"KVUFNZ25BGZCJUSM","created_at":"2026-06-19T16:11:18.218806+00:00"},{"alias_kind":"pith_short_8","alias_value":"KVUFNZ25","created_at":"2026-06-19T16:11:18.218806+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2605.00941","citing_title":"Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00941","citing_title":"Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2601.15884","citing_title":"Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2604.16955","citing_title":"Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E","json":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E.json","graph_json":"https://pith.science/api/pith-number/KVUFNZ25BGZCJUSM6WY5O7AO7E/graph.json","events_json":"https://pith.science/api/pith-number/KVUFNZ25BGZCJUSM6WY5O7AO7E/events.json","paper":"https://pith.science/paper/KVUFNZ25"},"agent_actions":{"view_html":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E","download_json":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E.json","view_paper":"https://pith.science/paper/KVUFNZ25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.09185&json=true","fetch_graph":"https://pith.science/api/pith-number/KVUFNZ25BGZCJUSM6WY5O7AO7E/graph.json","fetch_events":"https://pith.science/api/pith-number/KVUFNZ25BGZCJUSM6WY5O7AO7E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E/action/storage_attestation","attest_author":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E/action/author_attestation","sign_citation":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E/action/citation_signature","submit_replication":"https://pith.science/pith/KVUFNZ25BGZCJUSM6WY5O7AO7E/action/replication_record"}},"created_at":"2026-06-19T16:11:18.218806+00:00","updated_at":"2026-06-19T16:11:18.218806+00:00"}