{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VFUTJYBM5XPKBMVSDTFRQ7D256","short_pith_number":"pith:VFUTJYBM","schema_version":"1.0","canonical_sha256":"a96934e02ceddea0b2b21ccb187c7aef9508f8a7921d831847ddbf8d18710dd8","source":{"kind":"arxiv","id":"2403.06128","version":1},"attestation_state":"computed","paper":{"title":"Low-dose CT Denoising with Language-engaged Dual-space Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chenhui Wang, Chuang Niu, Ge Wang, Hongming Shan, Tao Chen, Zhihao Chen","submitted_at":"2024-03-10T08:21:50Z","abstract_excerpt":"While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability. To alleviate these issues, we propose a plug-and-play Language-Engaged Dual-space Alignment loss (LEDA) to optimize low-dose CT denoising models. Our idea is to leverage large language models (LLMs) to align denoised CT and normal dose CT images in both the continuous perceptual space and discrete semantic space, which is the first LLM-based scheme for low-dose CT denoising. LEDA involves two steps: the first is to pretr"},"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":"2403.06128","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-03-10T08:21:50Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"6890840986811d2a5d4d5833c77cb9e9a540d011a0fbb843cea49f9763256188","abstract_canon_sha256":"adc7f8359649f8023f78e08b35ee9507be9db5b4f4e773c18e213237b8bc1b17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:18.566633Z","signature_b64":"LegYZHQXW+Uzd8BxRub43vubnSPdHPlNBdabjJa2vjPqQacVMk4X3qzskoSbG3UNTQYSPkEhJWAL+M3v9Gr7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a96934e02ceddea0b2b21ccb187c7aef9508f8a7921d831847ddbf8d18710dd8","last_reissued_at":"2026-07-05T07:54:18.566141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:18.566141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Low-dose CT Denoising with Language-engaged Dual-space Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chenhui Wang, Chuang Niu, Ge Wang, Hongming Shan, Tao Chen, Zhihao Chen","submitted_at":"2024-03-10T08:21:50Z","abstract_excerpt":"While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability. To alleviate these issues, we propose a plug-and-play Language-Engaged Dual-space Alignment loss (LEDA) to optimize low-dose CT denoising models. Our idea is to leverage large language models (LLMs) to align denoised CT and normal dose CT images in both the continuous perceptual space and discrete semantic space, which is the first LLM-based scheme for low-dose CT denoising. LEDA involves two steps: the first is to pretr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06128","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/2403.06128/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":"2403.06128","created_at":"2026-07-05T07:54:18.566211+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06128v1","created_at":"2026-07-05T07:54:18.566211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06128","created_at":"2026-07-05T07:54:18.566211+00:00"},{"alias_kind":"pith_short_12","alias_value":"VFUTJYBM5XPK","created_at":"2026-07-05T07:54:18.566211+00:00"},{"alias_kind":"pith_short_16","alias_value":"VFUTJYBM5XPKBMVS","created_at":"2026-07-05T07:54:18.566211+00:00"},{"alias_kind":"pith_short_8","alias_value":"VFUTJYBM","created_at":"2026-07-05T07:54:18.566211+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/VFUTJYBM5XPKBMVSDTFRQ7D256","json":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256.json","graph_json":"https://pith.science/api/pith-number/VFUTJYBM5XPKBMVSDTFRQ7D256/graph.json","events_json":"https://pith.science/api/pith-number/VFUTJYBM5XPKBMVSDTFRQ7D256/events.json","paper":"https://pith.science/paper/VFUTJYBM"},"agent_actions":{"view_html":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256","download_json":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256.json","view_paper":"https://pith.science/paper/VFUTJYBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06128&json=true","fetch_graph":"https://pith.science/api/pith-number/VFUTJYBM5XPKBMVSDTFRQ7D256/graph.json","fetch_events":"https://pith.science/api/pith-number/VFUTJYBM5XPKBMVSDTFRQ7D256/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256/action/storage_attestation","attest_author":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256/action/author_attestation","sign_citation":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256/action/citation_signature","submit_replication":"https://pith.science/pith/VFUTJYBM5XPKBMVSDTFRQ7D256/action/replication_record"}},"created_at":"2026-07-05T07:54:18.566211+00:00","updated_at":"2026-07-05T07:54:18.566211+00:00"}