{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","short_pith_number":"pith:J62VYN6A","canonical_record":{"source":{"id":"2607.22139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-24T09:35:18Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"e02fd174f52d6e0d72bfab345ddf21481f18d84f5c9c46dc109185b1934f135b","abstract_canon_sha256":"fba038fb0f38629fc0cda17183fa59dfd7bd84041aa4a46cc046c02c6d0f53d4"},"schema_version":"1.0"},"canonical_sha256":"4fb55c37c04203ba11d06a031ffd81c9120c97742bbdd2e46aa01e95ba22f91c","source":{"kind":"arxiv","id":"2607.22139","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22139","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22139v1","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22139","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"pith_short_12","alias_value":"J62VYN6AIIB3","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"pith_short_16","alias_value":"J62VYN6AIIB3UEOQ","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"pith_short_8","alias_value":"J62VYN6A","created_at":"2026-07-27T01:20:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","target":"record","payload":{"canonical_record":{"source":{"id":"2607.22139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-24T09:35:18Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"e02fd174f52d6e0d72bfab345ddf21481f18d84f5c9c46dc109185b1934f135b","abstract_canon_sha256":"fba038fb0f38629fc0cda17183fa59dfd7bd84041aa4a46cc046c02c6d0f53d4"},"schema_version":"1.0"},"canonical_sha256":"4fb55c37c04203ba11d06a031ffd81c9120c97742bbdd2e46aa01e95ba22f91c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T01:20:50.113031Z","signature_b64":"aYmeLnXB6mIbN9Rs6zs2GYwq8SEOIKg0gEw7ll2gPIxQ74QNih9qIXqjSBNStEqVTxGp5+rw6JRuxXC2Kqj6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fb55c37c04203ba11d06a031ffd81c9120c97742bbdd2e46aa01e95ba22f91c","last_reissued_at":"2026-07-27T01:20:50.112235Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T01:20:50.112235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.22139","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":"2026-07-27T01:20:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"larnDMhtcjmqu70EVb4kCDQkQw7Qqym4Bv0AIrPXAX8Defxh5cBSCWqfp9wgrctt8Hx+ZDYGu3AB+TsYDwQqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:30:29.511926Z"},"content_sha256":"7332c824fc59e6691cbf12a3ece8b059c565c9425ba05ca0bcf5bba473e1adbb","schema_version":"1.0","event_id":"sha256:7332c824fc59e6691cbf12a3ece8b059c565c9425ba05ca0bcf5bba473e1adbb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Adam Brzeski, Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Natalia Zieli\\'nska, Rados{\\l}aw Targo\\'nski, Tomasz Dziubich, Tomasz Figatowski","submitted_at":"2026-07-24T09:35:18Z","abstract_excerpt":"Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols. In this work we demonstrate a new benchmark for the assessment of deep learning models which densely classify pixels of coronary angiograms to one of SYNTAX classes (or background). The evaluation covers 24 distinct architectures starting with classic convnets to recent state-space-based vision algorithms. We release CARDIAG - a multi-center, multi-label dataset which we carefully split to reliably compute metrics, accounting for d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22139","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.22139/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-27T01:20:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LTBoDscsnciN9p27YT/8UV7XvZLEaGVeLOlVuhBt+fBvnm3BjdP3GBmoj0lt4zhCKEoVsqg/PXyzFD1jfP9fDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:30:29.512692Z"},"content_sha256":"4f2cf45f564301d3d23529fb7113469fc7e9fdcdc1c986a282dbaebc6e076d06","schema_version":"1.0","event_id":"sha256:4f2cf45f564301d3d23529fb7113469fc7e9fdcdc1c986a282dbaebc6e076d06"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/978-3-031-43901-8_39) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"S. Royet al., “MedNeXt: Transformer- driven scaling of convnets for medical im- age segmentation,” inProc. Med. Image Comput. Comput.-Assist. Interv. (MIC- CAI), Vancouver, BC, Canada, 2023, pp. 405–415, doi: 10.1007/978-3-031-43901- 8_39","arxiv_id":"2607.22139","detector":"doi_compliance","evidence":{"ref_index":45,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1007/978-3-031-43901-","reconstructed_doi":"10.1007/978-3-031-43901-8_39"},"severity":"advisory","ref_index":45,"audited_at":"2026-08-01T05:48:47.075267Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-031-43901-8_39","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"a4d32bacb86ca0da6092d298e2f64981535db5c021f1c32c22f28b0acb0840ca","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15215,"payload_sha256":"e341810cd6cf06c3cb40f75ce0956b836ba845b5f145fae8ae4deecd3fb215e0","signature_b64":"cfonFmxqHl2svaW8sQfZGPV2JtUeHeYAKu2mE/YpiPpwRZoFYCZK/P5sHorARyPQOcbhulr42tiPlu0z8D+3AQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T05:53:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ALdr4HSGgTyTYsvC0Mj/G0c3bzHox9alY+l2dRSc+/oQRs8Otw9sFg7tT9sjvQBrISpaITJVmPbUhKm4vu8Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:30:29.535516Z"},"content_sha256":"df145c02eaeff4fc3c3d68459660ae5bf16a78d332f8828427f43003aba2fbaa","schema_version":"1.0","event_id":"sha256:df145c02eaeff4fc3c3d68459660ae5bf16a78d332f8828427f43003aba2fbaa"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/978-3-030-01228-1_26) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” inProc. Eur. Conf. Comput. Vis. (ECCV), Munich, Germany, 2018, pp. 432–448, doi: 10.1007/978-3- 030-01228-1_26","arxiv_id":"2607.22139","detector":"doi_compliance","evidence":{"ref_index":37,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1007/978-3-","reconstructed_doi":"10.1007/978-3-030-01228-1_26"},"severity":"advisory","ref_index":37,"audited_at":"2026-08-01T05:48:47.075267Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-030-01228-1_26","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"ff44aa1886beca73ee7e8cbc1b4cda19b0e59efe8e3b4a776982bb383f8ca0ec","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15214,"payload_sha256":"c51127ace7f8412ce5b1f69a435cc34be29de53f04e915b8ac6b393560643ae5","signature_b64":"BOAYLC1ancM9atS5ddi8bgwd4Rhx2OMBuMqIp2DP9C8Phh+85AJhORbDBC6FwYGh/NBJOec/09uDYF7VwUImDw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T05:53:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sbHTWNdBnemRbyB6D0IHAgYlED6Nx9JwVPEaTJL7vHWwjazzlbGKw7+oI/uHfdcGcVPswtHgKSX/+0P8hS9YAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:30:29.536186Z"},"content_sha256":"059596c78cbefe3fbe7591019e54988a3c3396f3e591b39dd6afb72f6ac22ccf","schema_version":"1.0","event_id":"sha256:059596c78cbefe3fbe7591019e54988a3c3396f3e591b39dd6afb72f6ac22ccf"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/978-3-319-24574-4_28) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” inProc. Med. Image Comput. Comput.-Assist. In- terv. (MICCAI), Munich, Germany, 2015, pp. 234–241, doi: 10.1007/978-3-319- 24574-4_2","arxiv_id":"2607.22139","detector":"doi_compliance","evidence":{"ref_index":31,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1007/978-3-319-","reconstructed_doi":"10.1007/978-3-319-24574-4_28"},"severity":"advisory","ref_index":31,"audited_at":"2026-08-01T05:48:47.075267Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-319-24574-4_28","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"3961b4555c83bd76638ce5b964aee66f3f0ad65b3503fef2477cb9a259d92aeb","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15213,"payload_sha256":"60bc34bc22a88b3cb152c87f1a42984ab293bf1b5bd260e0f477a4ee2f311e6f","signature_b64":"D4O6PU1j0y232jPiR8ByMlb8ggq6MVjRnclQVCDa0HLLxqP57XVPJtgwib4DqMp6I4e1bO+exoRVvNrugUz8CQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T05:53:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"opl0Nlj1/HgMl2mR5E7+kmwfsPERToxCZMgR2HkwgxlwpTuEg7VpxIDzkpoOocpWfyQCP04hXZwVomA4drI7Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:30:29.536927Z"},"content_sha256":"e268c601f393365aa51aa9f303fd3866135cb094be1d96ba5c2cd0f9cd4c90fd","schema_version":"1.0","event_id":"sha256:e268c601f393365aa51aa9f303fd3866135cb094be1d96ba5c2cd0f9cd4c90fd"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1038/s41597-025-04676-8) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"I. Kruzhilovet al., “CoronaryDominance: Angiogram dataset for coronary domi- nance classification,”Sci. Data, vol. 12, no. 1, Feb. 2025, doi: 10.1038/s41597-025- 04676-8","arxiv_id":"2607.22139","detector":"doi_compliance","evidence":{"ref_index":27,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1038/s41597-025-","reconstructed_doi":"10.1038/s41597-025-04676-8"},"severity":"advisory","ref_index":27,"audited_at":"2026-08-01T05:48:47.075267Z","event_type":"pith.integrity.v1","detected_doi":"10.1038/s41597-025-04676-8","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"59b78e803732ad9002b2ae624cbc88f909ed5c0e8cb5beaf432167293ef005f5","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15212,"payload_sha256":"3a37ac927bcad61e0a4a9a94b5f08f94215fff6f8742aca83f08f710fc35e538","signature_b64":"vLLxZr+cFdLaGot/VxjbTr1dRbmsilVsKpJf12zKsbpHSZ44zvPP2EoSi+ky8ZPINWbIeBX3v7avb2ZQ148VCA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T05:53:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lT0SOJpTCCJNQaGcrFv3V4aHOvQRWjhkmkGFdrxJ1WrARmnVLIeQHJgX4Ycp8/hF4IJl6fU+rau8lQiw7s3YCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:30:29.537598Z"},"content_sha256":"f2e9fb4f5313804e45bd74c8b8065742c9694e421de64abc8b02172eed0d86b6","schema_version":"1.0","event_id":"sha256:f2e9fb4f5313804e45bd74c8b8065742c9694e421de64abc8b02172eed0d86b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J62VYN6AIIB3UEOQNIBR77MBZE/bundle.json","state_url":"https://pith.science/pith/J62VYN6AIIB3UEOQNIBR77MBZE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J62VYN6AIIB3UEOQNIBR77MBZE/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":"2026-08-05T12:30:29Z","links":{"resolver":"https://pith.science/pith/J62VYN6AIIB3UEOQNIBR77MBZE","bundle":"https://pith.science/pith/J62VYN6AIIB3UEOQNIBR77MBZE/bundle.json","state":"https://pith.science/pith/J62VYN6AIIB3UEOQNIBR77MBZE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J62VYN6AIIB3UEOQNIBR77MBZE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:J62VYN6AIIB3UEOQNIBR77MBZE","merge_version":"pith-open-graph-merge-v1","event_count":6,"valid_event_count":6,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fba038fb0f38629fc0cda17183fa59dfd7bd84041aa4a46cc046c02c6d0f53d4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-24T09:35:18Z","title_canon_sha256":"e02fd174f52d6e0d72bfab345ddf21481f18d84f5c9c46dc109185b1934f135b"},"schema_version":"1.0","source":{"id":"2607.22139","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22139","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22139v1","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22139","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"pith_short_12","alias_value":"J62VYN6AIIB3","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"pith_short_16","alias_value":"J62VYN6AIIB3UEOQ","created_at":"2026-07-27T01:20:50Z"},{"alias_kind":"pith_short_8","alias_value":"J62VYN6A","created_at":"2026-07-27T01:20:50Z"}],"graph_snapshots":[{"event_id":"sha256:4f2cf45f564301d3d23529fb7113469fc7e9fdcdc1c986a282dbaebc6e076d06","target":"graph","created_at":"2026-07-27T01:20:50Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.22139/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols. In this work we demonstrate a new benchmark for the assessment of deep learning models which densely classify pixels of coronary angiograms to one of SYNTAX classes (or background). The evaluation covers 24 distinct architectures starting with classic convnets to recent state-space-based vision algorithms. We release CARDIAG - a multi-center, multi-label dataset which we carefully split to reliably compute metrics, accounting for d","authors_text":"Adam Brzeski, Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Natalia Zieli\\'nska, Rados{\\l}aw Targo\\'nski, Tomasz Dziubich, Tomasz Figatowski","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-24T09:35:18Z","title":"CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22139","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":"sha256:7332c824fc59e6691cbf12a3ece8b059c565c9425ba05ca0bcf5bba473e1adbb","target":"record","created_at":"2026-07-27T01:20:50Z","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":"fba038fb0f38629fc0cda17183fa59dfd7bd84041aa4a46cc046c02c6d0f53d4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-24T09:35:18Z","title_canon_sha256":"e02fd174f52d6e0d72bfab345ddf21481f18d84f5c9c46dc109185b1934f135b"},"schema_version":"1.0","source":{"id":"2607.22139","kind":"arxiv","version":1}},"canonical_sha256":"4fb55c37c04203ba11d06a031ffd81c9120c97742bbdd2e46aa01e95ba22f91c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4fb55c37c04203ba11d06a031ffd81c9120c97742bbdd2e46aa01e95ba22f91c","first_computed_at":"2026-07-27T01:20:50.112235Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-27T01:20:50.112235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aYmeLnXB6mIbN9Rs6zs2GYwq8SEOIKg0gEw7ll2gPIxQ74QNih9qIXqjSBNStEqVTxGp5+rw6JRuxXC2Kqj6Bw==","signature_status":"signed_v1","signed_at":"2026-07-27T01:20:50.113031Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.22139","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:059596c78cbefe3fbe7591019e54988a3c3396f3e591b39dd6afb72f6ac22ccf","sha256:df145c02eaeff4fc3c3d68459660ae5bf16a78d332f8828427f43003aba2fbaa","sha256:e268c601f393365aa51aa9f303fd3866135cb094be1d96ba5c2cd0f9cd4c90fd","sha256:f2e9fb4f5313804e45bd74c8b8065742c9694e421de64abc8b02172eed0d86b6"]}],"invalid_events":[],"applied_event_ids":["sha256:7332c824fc59e6691cbf12a3ece8b059c565c9425ba05ca0bcf5bba473e1adbb","sha256:4f2cf45f564301d3d23529fb7113469fc7e9fdcdc1c986a282dbaebc6e076d06"],"state_sha256":"a233b52e3f499a16ea37f60c1b91e84e7f432f7a92f82f4be170eb27a1431e09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pX7RgJTUBVDVSnFnLojUw3V96pLI8asq3xjeuYxk0ywZipBf0vyWACJVhA0DKZhAgWIEUB1/zCmrp3CMmLK2Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T12:30:29.559934Z","bundle_sha256":"9f0bcad98d62372a2c7cc0892c77e1090522e85c02898f3bf5afbb1fd0d36257"}}