{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:DOV6LM3WWYP7CR4X7BJLHQQRYB","short_pith_number":"pith:DOV6LM3W","schema_version":"1.0","canonical_sha256":"1babe5b376b61ff14797f852b3c211c0528a78a7ad2ef432cbd392ec00c240fd","source":{"kind":"arxiv","id":"2205.01676","version":3},"attestation_state":"computed","paper":{"title":"FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Eytan Z. Blumenthal, Hadas Pizem, Ilan Oren, Ingeborg Stalmans, Jan Van Eijgen, Joachim A. Behar, Joshua Melamed, Or Abramovich","submitted_at":"2022-05-02T21:01:34Z","abstract_excerpt":"Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale"},"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":"2205.01676","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-05-02T21:01:34Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7986e42740e236a8909e23d1c8adb743bf936e23ed281b5e679632035fe098a2","abstract_canon_sha256":"3002ed89812d5845a486f679ac49e01b26cfbb3240c3fb119acc1033dc387690"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:16.472663Z","signature_b64":"bsSFC5CQdoEJYVQs/VzNJC/XkUHPGcsAB8MAGxCVt5JgMhPlhMPT3Tdoojnnkpmqy5uDTVVI4/1dOc5qnN/gAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1babe5b376b61ff14797f852b3c211c0528a78a7ad2ef432cbd392ec00c240fd","last_reissued_at":"2026-07-05T06:18:16.472203Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:16.472203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Eytan Z. Blumenthal, Hadas Pizem, Ilan Oren, Ingeborg Stalmans, Jan Van Eijgen, Joachim A. Behar, Joshua Melamed, Or Abramovich","submitted_at":"2022-05-02T21:01:34Z","abstract_excerpt":"Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01676","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.01676/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":"2205.01676","created_at":"2026-07-05T06:18:16.472275+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.01676v3","created_at":"2026-07-05T06:18:16.472275+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01676","created_at":"2026-07-05T06:18:16.472275+00:00"},{"alias_kind":"pith_short_12","alias_value":"DOV6LM3WWYP7","created_at":"2026-07-05T06:18:16.472275+00:00"},{"alias_kind":"pith_short_16","alias_value":"DOV6LM3WWYP7CR4X","created_at":"2026-07-05T06:18:16.472275+00:00"},{"alias_kind":"pith_short_8","alias_value":"DOV6LM3W","created_at":"2026-07-05T06:18:16.472275+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/DOV6LM3WWYP7CR4X7BJLHQQRYB","json":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB.json","graph_json":"https://pith.science/api/pith-number/DOV6LM3WWYP7CR4X7BJLHQQRYB/graph.json","events_json":"https://pith.science/api/pith-number/DOV6LM3WWYP7CR4X7BJLHQQRYB/events.json","paper":"https://pith.science/paper/DOV6LM3W"},"agent_actions":{"view_html":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB","download_json":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB.json","view_paper":"https://pith.science/paper/DOV6LM3W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.01676&json=true","fetch_graph":"https://pith.science/api/pith-number/DOV6LM3WWYP7CR4X7BJLHQQRYB/graph.json","fetch_events":"https://pith.science/api/pith-number/DOV6LM3WWYP7CR4X7BJLHQQRYB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB/action/storage_attestation","attest_author":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB/action/author_attestation","sign_citation":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB/action/citation_signature","submit_replication":"https://pith.science/pith/DOV6LM3WWYP7CR4X7BJLHQQRYB/action/replication_record"}},"created_at":"2026-07-05T06:18:16.472275+00:00","updated_at":"2026-07-05T06:18:16.472275+00:00"}