{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R3ARSXGZ4WENEVC3ZIWPUUCJ3I","short_pith_number":"pith:R3ARSXGZ","schema_version":"1.0","canonical_sha256":"8ec1195cd9e588d2545bca2cfa5049da28f7c05e5dff9c29cf643af8a3002ac2","source":{"kind":"arxiv","id":"2407.07372","version":2},"attestation_state":"computed","paper":{"title":"Multi-modal MRI Translation via Evidential Regression and Distribution Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jiyao Liu, Junjun He, Junzhi Ning, Lihao Liu, Ningsheng Xu, Shangqi Gao, Xiahai Zhuang, Xiao-Yong Zhang, Xin Gao, Yanzhou Su, Yuxin Li, Zhaohu Xing","submitted_at":"2024-07-10T05:17:01Z","abstract_excerpt":"Multi-modal Magnetic Resonance Imaging (MRI) translation leverages information from source MRI sequences to generate target modalities, enabling comprehensive diagnosis while overcoming the limitations of acquiring all sequences. While existing deep-learning-based multi-modal MRI translation methods have shown promising potential, they still face two key challenges: 1) lack of reliable uncertainty quantification for synthesized images, and 2) limited robustness when deployed across different medical centers. To address these challenges, we propose a novel framework that reformulates multi-moda"},"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":"2407.07372","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-07-10T05:17:01Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"11d47b9f1ad4040a377e553d936a5c60ee8e384d8e8f7f6c8811f61c7b2a1bf1","abstract_canon_sha256":"b35420e200b3b38421e764b0ec3d9ff99a14b257915527eb919bbca671a0ed9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:38.358084Z","signature_b64":"G0n9zHEkKf2A66jvqz0nU9kQ0RbfAGbEcZfdIcGhk9Al4rmm6n047VQSpE9tHrVcTaie29P2EUs76x2go1mGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ec1195cd9e588d2545bca2cfa5049da28f7c05e5dff9c29cf643af8a3002ac2","last_reissued_at":"2026-07-05T11:04:38.357568Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:38.357568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-modal MRI Translation via Evidential Regression and Distribution Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jiyao Liu, Junjun He, Junzhi Ning, Lihao Liu, Ningsheng Xu, Shangqi Gao, Xiahai Zhuang, Xiao-Yong Zhang, Xin Gao, Yanzhou Su, Yuxin Li, Zhaohu Xing","submitted_at":"2024-07-10T05:17:01Z","abstract_excerpt":"Multi-modal Magnetic Resonance Imaging (MRI) translation leverages information from source MRI sequences to generate target modalities, enabling comprehensive diagnosis while overcoming the limitations of acquiring all sequences. While existing deep-learning-based multi-modal MRI translation methods have shown promising potential, they still face two key challenges: 1) lack of reliable uncertainty quantification for synthesized images, and 2) limited robustness when deployed across different medical centers. To address these challenges, we propose a novel framework that reformulates multi-moda"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07372","kind":"arxiv","version":2},"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/2407.07372/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":"2407.07372","created_at":"2026-07-05T11:04:38.357627+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.07372v2","created_at":"2026-07-05T11:04:38.357627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07372","created_at":"2026-07-05T11:04:38.357627+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3ARSXGZ4WEN","created_at":"2026-07-05T11:04:38.357627+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3ARSXGZ4WENEVC3","created_at":"2026-07-05T11:04:38.357627+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3ARSXGZ","created_at":"2026-07-05T11:04:38.357627+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/R3ARSXGZ4WENEVC3ZIWPUUCJ3I","json":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I.json","graph_json":"https://pith.science/api/pith-number/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/graph.json","events_json":"https://pith.science/api/pith-number/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/events.json","paper":"https://pith.science/paper/R3ARSXGZ"},"agent_actions":{"view_html":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I","download_json":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I.json","view_paper":"https://pith.science/paper/R3ARSXGZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.07372&json=true","fetch_graph":"https://pith.science/api/pith-number/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/graph.json","fetch_events":"https://pith.science/api/pith-number/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/action/storage_attestation","attest_author":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/action/author_attestation","sign_citation":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/action/citation_signature","submit_replication":"https://pith.science/pith/R3ARSXGZ4WENEVC3ZIWPUUCJ3I/action/replication_record"}},"created_at":"2026-07-05T11:04:38.357627+00:00","updated_at":"2026-07-05T11:04:38.357627+00:00"}