{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:SIZ6OLGF4WN4Q6OAWJEUHKZEIY","short_pith_number":"pith:SIZ6OLGF","canonical_record":{"source":{"id":"1907.02742","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-05T09:35:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9afc736f6eb447d4f703c319a0e2cf25997e158e3d6de846553fd1158c35b8fa","abstract_canon_sha256":"54ef9885e024bf52dda8f73c070555688a98eb35c666e364e06ce7e0df2bacc5"},"schema_version":"1.0"},"canonical_sha256":"9233e72cc5e59bc879c0b24943ab24460e21eafef049284e218d26d963b83f73","source":{"kind":"arxiv","id":"1907.02742","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.02742","created_at":"2026-05-17T23:41:23Z"},{"alias_kind":"arxiv_version","alias_value":"1907.02742v1","created_at":"2026-05-17T23:41:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.02742","created_at":"2026-05-17T23:41:23Z"},{"alias_kind":"pith_short_12","alias_value":"SIZ6OLGF4WN4","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"SIZ6OLGF4WN4Q6OA","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"SIZ6OLGF","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:SIZ6OLGF4WN4Q6OAWJEUHKZEIY","target":"record","payload":{"canonical_record":{"source":{"id":"1907.02742","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-05T09:35:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9afc736f6eb447d4f703c319a0e2cf25997e158e3d6de846553fd1158c35b8fa","abstract_canon_sha256":"54ef9885e024bf52dda8f73c070555688a98eb35c666e364e06ce7e0df2bacc5"},"schema_version":"1.0"},"canonical_sha256":"9233e72cc5e59bc879c0b24943ab24460e21eafef049284e218d26d963b83f73","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:23.329829Z","signature_b64":"14OOEqjbM0ynd3g7YOkPwmPNnLhMGz/vIm5+aQS+gdjZ2zMpUtoyfNWgm/M5SqthNEVjJfbNFauzAHrDADenCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9233e72cc5e59bc879c0b24943ab24460e21eafef049284e218d26d963b83f73","last_reissued_at":"2026-05-17T23:41:23.329148Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:23.329148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.02742","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":"2026-05-17T23:41:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NJ79dmq4DiQh7pNHlpRH12qOvkdBNDhzqpc+zyU72ghpsIeZplwVqNX5IV+Xi1kuew6mus7SWDY0XmqncXe8AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:36:48.900013Z"},"content_sha256":"90d87339376847841ae1d6e0207209177bfaa809bd42c7a93db498a78ec224dc","schema_version":"1.0","event_id":"sha256:90d87339376847841ae1d6e0207209177bfaa809bd42c7a93db498a78ec224dc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:SIZ6OLGF4WN4Q6OAWJEUHKZEIY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Domenec Puig, Farhan Akram, Hatem A. Rashwan, Md. Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Nidhi Pandey, Vivek Kumar Singh","submitted_at":"2019-07-05T09:35:38Z","abstract_excerpt":"In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization. In the generator network of the adversarial framework, spatial pyramid pooling, kernel factorization and squeeze excitation block are employed to enhance the feature representation in spatial domain on different scales with reduced computational complexity. In turn, the discriminator network of the adversarial framework is formulated by combining convolutional layers with an additional squeeze excitation block to differe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.02742","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":"2026-05-17T23:41:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"71TtbkYlNh5nKig/PQteOx2b/pmQk5KFAfmuXNAnfw7RBJmJ/X7mVCw5dCdHxNAHwuqdLzbwvJ/7cX7jOr4IAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:36:48.900479Z"},"content_sha256":"95d137abacbcb75fe5693063297408cd728ce6c7ba6646316559c0d267fa2acb","schema_version":"1.0","event_id":"sha256:95d137abacbcb75fe5693063297408cd728ce6c7ba6646316559c0d267fa2acb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY/bundle.json","state_url":"https://pith.science/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY/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":"2026-05-27T00:36:48Z","links":{"resolver":"https://pith.science/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY","bundle":"https://pith.science/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY/bundle.json","state":"https://pith.science/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SIZ6OLGF4WN4Q6OAWJEUHKZEIY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SIZ6OLGF4WN4Q6OAWJEUHKZEIY","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":"54ef9885e024bf52dda8f73c070555688a98eb35c666e364e06ce7e0df2bacc5","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-05T09:35:38Z","title_canon_sha256":"9afc736f6eb447d4f703c319a0e2cf25997e158e3d6de846553fd1158c35b8fa"},"schema_version":"1.0","source":{"id":"1907.02742","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.02742","created_at":"2026-05-17T23:41:23Z"},{"alias_kind":"arxiv_version","alias_value":"1907.02742v1","created_at":"2026-05-17T23:41:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.02742","created_at":"2026-05-17T23:41:23Z"},{"alias_kind":"pith_short_12","alias_value":"SIZ6OLGF4WN4","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"SIZ6OLGF4WN4Q6OA","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"SIZ6OLGF","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:95d137abacbcb75fe5693063297408cd728ce6c7ba6646316559c0d267fa2acb","target":"graph","created_at":"2026-05-17T23:41:23Z","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":"In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization. In the generator network of the adversarial framework, spatial pyramid pooling, kernel factorization and squeeze excitation block are employed to enhance the feature representation in spatial domain on different scales with reduced computational complexity. In turn, the discriminator network of the adversarial framework is formulated by combining convolutional layers with an additional squeeze excitation block to differe","authors_text":"Domenec Puig, Farhan Akram, Hatem A. Rashwan, Md. Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Nidhi Pandey, Vivek Kumar Singh","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-05T09:35:38Z","title":"Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.02742","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":"sha256:90d87339376847841ae1d6e0207209177bfaa809bd42c7a93db498a78ec224dc","target":"record","created_at":"2026-05-17T23:41:23Z","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":"54ef9885e024bf52dda8f73c070555688a98eb35c666e364e06ce7e0df2bacc5","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-05T09:35:38Z","title_canon_sha256":"9afc736f6eb447d4f703c319a0e2cf25997e158e3d6de846553fd1158c35b8fa"},"schema_version":"1.0","source":{"id":"1907.02742","kind":"arxiv","version":1}},"canonical_sha256":"9233e72cc5e59bc879c0b24943ab24460e21eafef049284e218d26d963b83f73","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9233e72cc5e59bc879c0b24943ab24460e21eafef049284e218d26d963b83f73","first_computed_at":"2026-05-17T23:41:23.329148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:23.329148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"14OOEqjbM0ynd3g7YOkPwmPNnLhMGz/vIm5+aQS+gdjZ2zMpUtoyfNWgm/M5SqthNEVjJfbNFauzAHrDADenCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:23.329829Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.02742","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90d87339376847841ae1d6e0207209177bfaa809bd42c7a93db498a78ec224dc","sha256:95d137abacbcb75fe5693063297408cd728ce6c7ba6646316559c0d267fa2acb"],"state_sha256":"58b908fbe4e4e43417b5fa9f1355f4e5668ff458e56174c83b58e51d009b2a22"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y4zsKWZ9MDu+9SVITpYfHVYcYpDjZcEshoa3MC7YJATPCbOYb4wNYRF8ZDSIaW2DZT2m53V2PmaJPGCLSjReDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T00:36:48.903987Z","bundle_sha256":"27dc104baffc132768063f65db7574cb76abaf5f91ba579b8b43eeafd881f4c1"}}