{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MNLJ5M35VKYK7ZUWWHAR3KPBOG","short_pith_number":"pith:MNLJ5M35","schema_version":"1.0","canonical_sha256":"63569eb37daab0afe696b1c11da9e171b96d3fa9f4036277f2c2d9bf9634d0de","source":{"kind":"arxiv","id":"2311.18496","version":2},"attestation_state":"computed","paper":{"title":"Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shuai Wang, Tengjin Weng, Yang Shen, Zhidong Zhao, Zhiming Cheng","submitted_at":"2023-11-30T12:17:16Z","abstract_excerpt":"Optic disc and cup segmentation plays a crucial role in automating the screening and diagnosis of optic glaucoma. While data-driven convolutional neural networks (CNNs) show promise in this area, the inherent ambiguity of segmenting objects and background boundaries in the task of optic disc and cup segmentation leads to noisy annotations that impact model performance. To address this, we propose an innovative label-denoising method of Multiple Pseudo-labels Noise-aware Network (MPNN) for accurate optic disc and cup segmentation. Specifically, the Multiple Pseudo-labels Generation and Guided D"},"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":"2311.18496","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-30T12:17:16Z","cross_cats_sorted":[],"title_canon_sha256":"5cbd5979b1c5bb3295fbe9dad97be349246213beea213939b1f285401350c23c","abstract_canon_sha256":"5f38a745afc04d40ccf5acd54ffcbaee130a43c369e4b9db8b0d378fe28f10ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:19.395188Z","signature_b64":"8twYvN1bpANqtSrQlyfkSUl/ngNLfNQsMxkbDnocfp0HLt79bTIeLJjw8ytKgSv3hw8Hn+ehi9BA0y4c7tydBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63569eb37daab0afe696b1c11da9e171b96d3fa9f4036277f2c2d9bf9634d0de","last_reissued_at":"2026-07-05T07:56:19.394595Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:19.394595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shuai Wang, Tengjin Weng, Yang Shen, Zhidong Zhao, Zhiming Cheng","submitted_at":"2023-11-30T12:17:16Z","abstract_excerpt":"Optic disc and cup segmentation plays a crucial role in automating the screening and diagnosis of optic glaucoma. While data-driven convolutional neural networks (CNNs) show promise in this area, the inherent ambiguity of segmenting objects and background boundaries in the task of optic disc and cup segmentation leads to noisy annotations that impact model performance. To address this, we propose an innovative label-denoising method of Multiple Pseudo-labels Noise-aware Network (MPNN) for accurate optic disc and cup segmentation. Specifically, the Multiple Pseudo-labels Generation and Guided D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.18496","kind":"arxiv","version":2},"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/2311.18496/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":"2311.18496","created_at":"2026-07-05T07:56:19.394662+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.18496v2","created_at":"2026-07-05T07:56:19.394662+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.18496","created_at":"2026-07-05T07:56:19.394662+00:00"},{"alias_kind":"pith_short_12","alias_value":"MNLJ5M35VKYK","created_at":"2026-07-05T07:56:19.394662+00:00"},{"alias_kind":"pith_short_16","alias_value":"MNLJ5M35VKYK7ZUW","created_at":"2026-07-05T07:56:19.394662+00:00"},{"alias_kind":"pith_short_8","alias_value":"MNLJ5M35","created_at":"2026-07-05T07:56:19.394662+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/MNLJ5M35VKYK7ZUWWHAR3KPBOG","json":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG.json","graph_json":"https://pith.science/api/pith-number/MNLJ5M35VKYK7ZUWWHAR3KPBOG/graph.json","events_json":"https://pith.science/api/pith-number/MNLJ5M35VKYK7ZUWWHAR3KPBOG/events.json","paper":"https://pith.science/paper/MNLJ5M35"},"agent_actions":{"view_html":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG","download_json":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG.json","view_paper":"https://pith.science/paper/MNLJ5M35","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.18496&json=true","fetch_graph":"https://pith.science/api/pith-number/MNLJ5M35VKYK7ZUWWHAR3KPBOG/graph.json","fetch_events":"https://pith.science/api/pith-number/MNLJ5M35VKYK7ZUWWHAR3KPBOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG/action/storage_attestation","attest_author":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG/action/author_attestation","sign_citation":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG/action/citation_signature","submit_replication":"https://pith.science/pith/MNLJ5M35VKYK7ZUWWHAR3KPBOG/action/replication_record"}},"created_at":"2026-07-05T07:56:19.394662+00:00","updated_at":"2026-07-05T07:56:19.394662+00:00"}