{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:T7DLXAX6FUFII6YDKNSQP5HGO2","short_pith_number":"pith:T7DLXAX6","schema_version":"1.0","canonical_sha256":"9fc6bb82fe2d0a847b03536507f4e676b4a827b4dc9c89c16d77245866c11cd0","source":{"kind":"arxiv","id":"2504.00264","version":1},"attestation_state":"computed","paper":{"title":"DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"eess.IV","authors_text":"Basar Demir, Boris Mailhe, Eric Z. Chen, Lin Zhao, Shanhui Sun, Terrence Chen, Xiao Chen, Yikang Liu","submitted_at":"2025-03-31T22:15:53Z","abstract_excerpt":"Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-frequency details. Our approach comprises three stages. First, we train a diffusion model on noisy images, using the outputs of a pretrained Blind-Spot Network as conditioning inputs. Next, we introduce a novel stabilized reverse sampling tech"},"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":"2504.00264","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-03-31T22:15:53Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"a8928fe2ec75b3d5da1a0a71161651101062b89f58742878403e462e2ddf4b1b","abstract_canon_sha256":"6ffd406d015245d379a2e7a5dbf01a25e18b588ff2b901faf7eca34f5d463b0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:23.234246Z","signature_b64":"ECSASQ+ccJG8I07m1O9lF0EfHLNVofBNlu7QyQp9qfn0v5fJpcemvRg56fhs9kkp1kO+uZ16ae4ndkkYYH46DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fc6bb82fe2d0a847b03536507f4e676b4a827b4dc9c89c16d77245866c11cd0","last_reissued_at":"2026-07-05T10:42:23.233766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:23.233766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"eess.IV","authors_text":"Basar Demir, Boris Mailhe, Eric Z. Chen, Lin Zhao, Shanhui Sun, Terrence Chen, Xiao Chen, Yikang Liu","submitted_at":"2025-03-31T22:15:53Z","abstract_excerpt":"Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-frequency details. Our approach comprises three stages. First, we train a diffusion model on noisy images, using the outputs of a pretrained Blind-Spot Network as conditioning inputs. Next, we introduce a novel stabilized reverse sampling tech"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.00264","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/2504.00264/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":"2504.00264","created_at":"2026-07-05T10:42:23.233825+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.00264v1","created_at":"2026-07-05T10:42:23.233825+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.00264","created_at":"2026-07-05T10:42:23.233825+00:00"},{"alias_kind":"pith_short_12","alias_value":"T7DLXAX6FUFI","created_at":"2026-07-05T10:42:23.233825+00:00"},{"alias_kind":"pith_short_16","alias_value":"T7DLXAX6FUFII6YD","created_at":"2026-07-05T10:42:23.233825+00:00"},{"alias_kind":"pith_short_8","alias_value":"T7DLXAX6","created_at":"2026-07-05T10:42:23.233825+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23200","citing_title":"NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2","json":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2.json","graph_json":"https://pith.science/api/pith-number/T7DLXAX6FUFII6YDKNSQP5HGO2/graph.json","events_json":"https://pith.science/api/pith-number/T7DLXAX6FUFII6YDKNSQP5HGO2/events.json","paper":"https://pith.science/paper/T7DLXAX6"},"agent_actions":{"view_html":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2","download_json":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2.json","view_paper":"https://pith.science/paper/T7DLXAX6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.00264&json=true","fetch_graph":"https://pith.science/api/pith-number/T7DLXAX6FUFII6YDKNSQP5HGO2/graph.json","fetch_events":"https://pith.science/api/pith-number/T7DLXAX6FUFII6YDKNSQP5HGO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2/action/storage_attestation","attest_author":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2/action/author_attestation","sign_citation":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2/action/citation_signature","submit_replication":"https://pith.science/pith/T7DLXAX6FUFII6YDKNSQP5HGO2/action/replication_record"}},"created_at":"2026-07-05T10:42:23.233825+00:00","updated_at":"2026-07-05T10:42:23.233825+00:00"}