{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IJK6Y2OKKI6ELU7TEPT2B2PZFO","short_pith_number":"pith:IJK6Y2OK","schema_version":"1.0","canonical_sha256":"4255ec69ca523c45d3f323e7a0e9f92b9389965d05a3f0d5b2ada13789d82556","source":{"kind":"arxiv","id":"2102.02662","version":1},"attestation_state":"computed","paper":{"title":"No-reference denoising of low-dose CT projections","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Alexey Chernyavskiy, Dmitry V. Dylov, Elvira Zainulina","submitted_at":"2021-02-03T13:51:33Z","abstract_excerpt":"Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising"},"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":"2102.02662","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-03T13:51:33Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"d7c0fea360e2f02232538b8af16c7f3434ba6d3dbbdc9a77bf9324cb4badfabd","abstract_canon_sha256":"85e55251bddf5ee3952b9fff448f270846e14efbb7998d3b6a94b555e554a14e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:12:50.555911Z","signature_b64":"ht/XD4rjxL4hMwcn961W22x+Eu80IjPpmiDyPcQXSNVoY0DkfeZWdn3nrLq0xMNLgq3wEIA8bMd6KR3JQXuBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4255ec69ca523c45d3f323e7a0e9f92b9389965d05a3f0d5b2ada13789d82556","last_reissued_at":"2026-07-05T02:12:50.555436Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:12:50.555436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"No-reference denoising of low-dose CT projections","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Alexey Chernyavskiy, Dmitry V. Dylov, Elvira Zainulina","submitted_at":"2021-02-03T13:51:33Z","abstract_excerpt":"Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.02662","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/2102.02662/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":"2102.02662","created_at":"2026-07-05T02:12:50.555481+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.02662v1","created_at":"2026-07-05T02:12:50.555481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.02662","created_at":"2026-07-05T02:12:50.555481+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJK6Y2OKKI6E","created_at":"2026-07-05T02:12:50.555481+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJK6Y2OKKI6ELU7T","created_at":"2026-07-05T02:12:50.555481+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJK6Y2OK","created_at":"2026-07-05T02:12:50.555481+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/IJK6Y2OKKI6ELU7TEPT2B2PZFO","json":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO.json","graph_json":"https://pith.science/api/pith-number/IJK6Y2OKKI6ELU7TEPT2B2PZFO/graph.json","events_json":"https://pith.science/api/pith-number/IJK6Y2OKKI6ELU7TEPT2B2PZFO/events.json","paper":"https://pith.science/paper/IJK6Y2OK"},"agent_actions":{"view_html":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO","download_json":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO.json","view_paper":"https://pith.science/paper/IJK6Y2OK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.02662&json=true","fetch_graph":"https://pith.science/api/pith-number/IJK6Y2OKKI6ELU7TEPT2B2PZFO/graph.json","fetch_events":"https://pith.science/api/pith-number/IJK6Y2OKKI6ELU7TEPT2B2PZFO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO/action/storage_attestation","attest_author":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO/action/author_attestation","sign_citation":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO/action/citation_signature","submit_replication":"https://pith.science/pith/IJK6Y2OKKI6ELU7TEPT2B2PZFO/action/replication_record"}},"created_at":"2026-07-05T02:12:50.555481+00:00","updated_at":"2026-07-05T02:12:50.555481+00:00"}