{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RDO4PH7BO4ZIRJ7UD6YPH75WY2","short_pith_number":"pith:RDO4PH7B","schema_version":"1.0","canonical_sha256":"88ddc79fe1773288a7f41fb0f3ffb6c6953f2d9344ad8f567bf7c76565f9eefd","source":{"kind":"arxiv","id":"1805.07777","version":3},"attestation_state":"computed","paper":{"title":"DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.CV","authors_text":"Fan Xu, Fa Zhang, Lihua Li, Ming Fan, Mingshu Zhang, Pingyong Xu, Renmin Han, Xin Gao, Yu Li","submitted_at":"2018-05-20T15:28:56Z","abstract_excerpt":"Super-resolution fluorescence microscopy, with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures. Here we propose a novel deep learning guided Bayesian inference approach, DLBI, for the time-series analysis of high-de"},"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":"1805.07777","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-20T15:28:56Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"b27ca89d1eb44ab920b5b82948ebc99995582af041623e08217fd4d96fd0651b","abstract_canon_sha256":"3edfba22277ac8d053253a7fed70a033bc4dfaa39f28006ee4143fc2ff0faae0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:39.753814Z","signature_b64":"RcPLXk24dBV73feNMQsCgJ2RPUxp9Qpon8+QlxpLEkV2Yi1jNHIPRGep7d/sou36c5tQrdNjSEgg10WtdeAGAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88ddc79fe1773288a7f41fb0f3ffb6c6953f2d9344ad8f567bf7c76565f9eefd","last_reissued_at":"2026-05-18T00:06:39.753303Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:39.753303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.CV","authors_text":"Fan Xu, Fa Zhang, Lihua Li, Ming Fan, Mingshu Zhang, Pingyong Xu, Renmin Han, Xin Gao, Yu Li","submitted_at":"2018-05-20T15:28:56Z","abstract_excerpt":"Super-resolution fluorescence microscopy, with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures. Here we propose a novel deep learning guided Bayesian inference approach, DLBI, for the time-series analysis of high-de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07777","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1805.07777","created_at":"2026-05-18T00:06:39.753409+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.07777v3","created_at":"2026-05-18T00:06:39.753409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07777","created_at":"2026-05-18T00:06:39.753409+00:00"},{"alias_kind":"pith_short_12","alias_value":"RDO4PH7BO4ZI","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RDO4PH7BO4ZIRJ7U","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RDO4PH7B","created_at":"2026-05-18T12:32:50.500415+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/RDO4PH7BO4ZIRJ7UD6YPH75WY2","json":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2.json","graph_json":"https://pith.science/api/pith-number/RDO4PH7BO4ZIRJ7UD6YPH75WY2/graph.json","events_json":"https://pith.science/api/pith-number/RDO4PH7BO4ZIRJ7UD6YPH75WY2/events.json","paper":"https://pith.science/paper/RDO4PH7B"},"agent_actions":{"view_html":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2","download_json":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2.json","view_paper":"https://pith.science/paper/RDO4PH7B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.07777&json=true","fetch_graph":"https://pith.science/api/pith-number/RDO4PH7BO4ZIRJ7UD6YPH75WY2/graph.json","fetch_events":"https://pith.science/api/pith-number/RDO4PH7BO4ZIRJ7UD6YPH75WY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2/action/storage_attestation","attest_author":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2/action/author_attestation","sign_citation":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2/action/citation_signature","submit_replication":"https://pith.science/pith/RDO4PH7BO4ZIRJ7UD6YPH75WY2/action/replication_record"}},"created_at":"2026-05-18T00:06:39.753409+00:00","updated_at":"2026-05-18T00:06:39.753409+00:00"}