{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HTMXP4K2ZMRPXPGP52KBBKVN6Y","short_pith_number":"pith:HTMXP4K2","canonical_record":{"source":{"id":"1810.13376","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-31T16:12:30Z","cross_cats_sorted":[],"title_canon_sha256":"3ece083ab901150da2b26d719d04d0854f8378a65f338a9ca9f79b00ce65657f","abstract_canon_sha256":"12e1c75cdaca6a7634e910530643cfa2c05988a2d121f37671049419c418e8de"},"schema_version":"1.0"},"canonical_sha256":"3cd977f15acb22fbbccfee9410aaadf63fdcddf94c836c8b679f60ee1ce5accb","source":{"kind":"arxiv","id":"1810.13376","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.13376","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"arxiv_version","alias_value":"1810.13376v1","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13376","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"pith_short_12","alias_value":"HTMXP4K2ZMRP","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HTMXP4K2ZMRPXPGP","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HTMXP4K2","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HTMXP4K2ZMRPXPGP52KBBKVN6Y","target":"record","payload":{"canonical_record":{"source":{"id":"1810.13376","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-31T16:12:30Z","cross_cats_sorted":[],"title_canon_sha256":"3ece083ab901150da2b26d719d04d0854f8378a65f338a9ca9f79b00ce65657f","abstract_canon_sha256":"12e1c75cdaca6a7634e910530643cfa2c05988a2d121f37671049419c418e8de"},"schema_version":"1.0"},"canonical_sha256":"3cd977f15acb22fbbccfee9410aaadf63fdcddf94c836c8b679f60ee1ce5accb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:49.501879Z","signature_b64":"bR7M0Mujnc8oR7mnqugPGBknV4ZVU4FtGmVKB4KTZpGd3eJFZet8Blz9AdPXNtvbMP6kgDKtsRtWyY/Wz/p5CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cd977f15acb22fbbccfee9410aaadf63fdcddf94c836c8b679f60ee1ce5accb","last_reissued_at":"2026-05-18T00:01:49.501175Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:49.501175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.13376","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"moDXeJXb752N3ZRANK7sSLtZ/KK8tE/GOupMJ5CP+ivqBRt6boUfEc8YxYvvQZMDA2fEDyaYUYOF7bGTZP7ZAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:18:43.666123Z"},"content_sha256":"4353defa5054a02a8dc3f83b4702f11de5091310320538980fc8b522b182df9a","schema_version":"1.0","event_id":"sha256:4353defa5054a02a8dc3f83b4702f11de5091310320538980fc8b522b182df9a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HTMXP4K2ZMRPXPGP52KBBKVN6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Performance assessment of the deep learning technologies in grading glaucoma severity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Han Liu, Jiantao Pu, Jian Zhang, Lei Wang, Yi Zhen","submitted_at":"2018-10-31T16:12:30Z","abstract_excerpt":"Objective: To validate and compare the performance of eight available deep learning architectures in grading the severity of glaucoma based on color fundus images. Materials and Methods: We retrospectively collected a dataset of 5978 fundus images and their glaucoma severities were annotated by the consensus of two experienced ophthalmologists. We preprocessed the images to generate global and local regions of interest (ROIs), namely the global field-of-view images and the local disc region images. We then divided the generated images into three independent sub-groups for training, validation,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13376","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bU2Dmt+ctu1OojFHvdPkiELEvkCaaqspn/pvJN/ECfF4qfZcFV8MO1XdfQ3DjmvK86w1i057aQLspYfCvMAICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:18:43.666483Z"},"content_sha256":"c6c1ebb336b020e4c9981e6ae09e0d9a4508e372df57496aee90d6cc9935801e","schema_version":"1.0","event_id":"sha256:c6c1ebb336b020e4c9981e6ae09e0d9a4508e372df57496aee90d6cc9935801e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y/bundle.json","state_url":"https://pith.science/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T19:18:43Z","links":{"resolver":"https://pith.science/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y","bundle":"https://pith.science/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y/bundle.json","state":"https://pith.science/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HTMXP4K2ZMRPXPGP52KBBKVN6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HTMXP4K2ZMRPXPGP52KBBKVN6Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"12e1c75cdaca6a7634e910530643cfa2c05988a2d121f37671049419c418e8de","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-31T16:12:30Z","title_canon_sha256":"3ece083ab901150da2b26d719d04d0854f8378a65f338a9ca9f79b00ce65657f"},"schema_version":"1.0","source":{"id":"1810.13376","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.13376","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"arxiv_version","alias_value":"1810.13376v1","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13376","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"pith_short_12","alias_value":"HTMXP4K2ZMRP","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HTMXP4K2ZMRPXPGP","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HTMXP4K2","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:c6c1ebb336b020e4c9981e6ae09e0d9a4508e372df57496aee90d6cc9935801e","target":"graph","created_at":"2026-05-18T00:01:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Objective: To validate and compare the performance of eight available deep learning architectures in grading the severity of glaucoma based on color fundus images. Materials and Methods: We retrospectively collected a dataset of 5978 fundus images and their glaucoma severities were annotated by the consensus of two experienced ophthalmologists. We preprocessed the images to generate global and local regions of interest (ROIs), namely the global field-of-view images and the local disc region images. We then divided the generated images into three independent sub-groups for training, validation,","authors_text":"Han Liu, Jiantao Pu, Jian Zhang, Lei Wang, Yi Zhen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-31T16:12:30Z","title":"Performance assessment of the deep learning technologies in grading glaucoma severity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13376","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4353defa5054a02a8dc3f83b4702f11de5091310320538980fc8b522b182df9a","target":"record","created_at":"2026-05-18T00:01:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"12e1c75cdaca6a7634e910530643cfa2c05988a2d121f37671049419c418e8de","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-10-31T16:12:30Z","title_canon_sha256":"3ece083ab901150da2b26d719d04d0854f8378a65f338a9ca9f79b00ce65657f"},"schema_version":"1.0","source":{"id":"1810.13376","kind":"arxiv","version":1}},"canonical_sha256":"3cd977f15acb22fbbccfee9410aaadf63fdcddf94c836c8b679f60ee1ce5accb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3cd977f15acb22fbbccfee9410aaadf63fdcddf94c836c8b679f60ee1ce5accb","first_computed_at":"2026-05-18T00:01:49.501175Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:49.501175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bR7M0Mujnc8oR7mnqugPGBknV4ZVU4FtGmVKB4KTZpGd3eJFZet8Blz9AdPXNtvbMP6kgDKtsRtWyY/Wz/p5CA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:49.501879Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.13376","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4353defa5054a02a8dc3f83b4702f11de5091310320538980fc8b522b182df9a","sha256:c6c1ebb336b020e4c9981e6ae09e0d9a4508e372df57496aee90d6cc9935801e"],"state_sha256":"f73a709d511255675f0762432cae62631775c9bfe6b5dce671ee5ba2c77baa24"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lt1afS9OQvSwIqEeVd/DkZ8cECuCdTEnK+JZmxUzw9fK1PR80Nz28yg5XGz4RSnY1Qxt980/ZvMh0B2k6ZuzBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T19:18:43.668482Z","bundle_sha256":"e2c7d264fcdf1189ef25ae3a5968c349a8bb7ef1f7dddec9abd3a8cde26cad35"}}