{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GBEFWGVZ7C2AAN6E2XIG5NN4BE","short_pith_number":"pith:GBEFWGVZ","canonical_record":{"source":{"id":"2507.17779","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-22T21:27:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ef61ac0febbb58c55b12d606d07330f7c7310ab8714ffdfbe8ae9ed41b74f226","abstract_canon_sha256":"22de0fb283d9ce08d1f6118a9db14935909e687a606acf5a807d4cd2499485cd"},"schema_version":"1.0"},"canonical_sha256":"30485b1ab9f8b40037c4d5d06eb5bc092b409d152e52e6f582a5d424b0e1e051","source":{"kind":"arxiv","id":"2507.17779","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.17779","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"arxiv_version","alias_value":"2507.17779v1","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17779","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_12","alias_value":"GBEFWGVZ7C2A","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_16","alias_value":"GBEFWGVZ7C2AAN6E","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_8","alias_value":"GBEFWGVZ","created_at":"2026-07-05T11:42:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GBEFWGVZ7C2AAN6E2XIG5NN4BE","target":"record","payload":{"canonical_record":{"source":{"id":"2507.17779","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-22T21:27:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ef61ac0febbb58c55b12d606d07330f7c7310ab8714ffdfbe8ae9ed41b74f226","abstract_canon_sha256":"22de0fb283d9ce08d1f6118a9db14935909e687a606acf5a807d4cd2499485cd"},"schema_version":"1.0"},"canonical_sha256":"30485b1ab9f8b40037c4d5d06eb5bc092b409d152e52e6f582a5d424b0e1e051","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:35.028427Z","signature_b64":"dJfD7uIyUgEF5NOoQGq54oij0xmJHc7IyleOiesHHUZeNKpSDtZ+D2z6B8VaS4XRabK07v7SjgI0NHjYY9VTDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30485b1ab9f8b40037c4d5d06eb5bc092b409d152e52e6f582a5d424b0e1e051","last_reissued_at":"2026-07-05T11:42:35.027889Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:35.027889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.17779","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":"2026-07-05T11:42:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JdiayWbwqKlZzQS+0cYAyc3+Og0KDXH0ap4yarSSATR9OnF+XTSHfJfkjXeDHYwD3qhljhQCQPUm3WOO3PsCAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:58:24.396989Z"},"content_sha256":"1937c1230054102b3f622fcb8b95ec7f3062a789d6925bb355172231e4376589","schema_version":"1.0","event_id":"sha256:1937c1230054102b3f622fcb8b95ec7f3062a789d6925bb355172231e4376589"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GBEFWGVZ7C2AAN6E2XIG5NN4BE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.QM","authors_text":"Bernard De Bruyne, Camille Challier, Denise Auberson, Dorina Thanou, Emmanuel Abb\\'e, Olivier M\\\"uller, Ortal Senouf, Pascal Frossard, Stephane Fournier, Thabo Mahendiran, Xiaowu Sun","submitted_at":"2025-07-22T21:27:34Z","abstract_excerpt":"Accurate segmentation of coronary arteries remains a significant challenge in clinical practice, hindering the ability to effectively diagnose and manage coronary artery disease. The lack of large, annotated datasets for model training exacerbates this issue, limiting the development of automated tools that could assist radiologists. To address this, we introduce CM-UNet, which leverages self-supervised pre-training on unannotated datasets and transfer learning on limited annotated data, enabling accurate disease detection while minimizing the need for extensive manual annotations. Fine-tuning"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17779","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/2507.17779/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":"2026-07-05T11:42:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6nlWpdGevSRY1nNQQabu784NgXh+7QOSogSotYQAHTx79wKlewNUiUY99/+XjTytfhHibnav6Qfmap7+24lkBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:58:24.397923Z"},"content_sha256":"bffc7ef927fd3ef275731b815104d699f6a73db1fa73819df651be9b2e20a4c9","schema_version":"1.0","event_id":"sha256:bffc7ef927fd3ef275731b815104d699f6a73db1fa73819df651be9b2e20a4c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE/bundle.json","state_url":"https://pith.science/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE/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":"2026-08-23T01:58:24Z","links":{"resolver":"https://pith.science/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE","bundle":"https://pith.science/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE/bundle.json","state":"https://pith.science/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GBEFWGVZ7C2AAN6E2XIG5NN4BE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GBEFWGVZ7C2AAN6E2XIG5NN4BE","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":"22de0fb283d9ce08d1f6118a9db14935909e687a606acf5a807d4cd2499485cd","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-22T21:27:34Z","title_canon_sha256":"ef61ac0febbb58c55b12d606d07330f7c7310ab8714ffdfbe8ae9ed41b74f226"},"schema_version":"1.0","source":{"id":"2507.17779","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.17779","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"arxiv_version","alias_value":"2507.17779v1","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17779","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_12","alias_value":"GBEFWGVZ7C2A","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_16","alias_value":"GBEFWGVZ7C2AAN6E","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_8","alias_value":"GBEFWGVZ","created_at":"2026-07-05T11:42:35Z"}],"graph_snapshots":[{"event_id":"sha256:bffc7ef927fd3ef275731b815104d699f6a73db1fa73819df651be9b2e20a4c9","target":"graph","created_at":"2026-07-05T11:42:35Z","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/2507.17779/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate segmentation of coronary arteries remains a significant challenge in clinical practice, hindering the ability to effectively diagnose and manage coronary artery disease. The lack of large, annotated datasets for model training exacerbates this issue, limiting the development of automated tools that could assist radiologists. To address this, we introduce CM-UNet, which leverages self-supervised pre-training on unannotated datasets and transfer learning on limited annotated data, enabling accurate disease detection while minimizing the need for extensive manual annotations. Fine-tuning","authors_text":"Bernard De Bruyne, Camille Challier, Denise Auberson, Dorina Thanou, Emmanuel Abb\\'e, Olivier M\\\"uller, Ortal Senouf, Pascal Frossard, Stephane Fournier, Thabo Mahendiran, Xiaowu Sun","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-22T21:27:34Z","title":"CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17779","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":"sha256:1937c1230054102b3f622fcb8b95ec7f3062a789d6925bb355172231e4376589","target":"record","created_at":"2026-07-05T11:42:35Z","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":"22de0fb283d9ce08d1f6118a9db14935909e687a606acf5a807d4cd2499485cd","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-22T21:27:34Z","title_canon_sha256":"ef61ac0febbb58c55b12d606d07330f7c7310ab8714ffdfbe8ae9ed41b74f226"},"schema_version":"1.0","source":{"id":"2507.17779","kind":"arxiv","version":1}},"canonical_sha256":"30485b1ab9f8b40037c4d5d06eb5bc092b409d152e52e6f582a5d424b0e1e051","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"30485b1ab9f8b40037c4d5d06eb5bc092b409d152e52e6f582a5d424b0e1e051","first_computed_at":"2026-07-05T11:42:35.027889Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:35.027889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dJfD7uIyUgEF5NOoQGq54oij0xmJHc7IyleOiesHHUZeNKpSDtZ+D2z6B8VaS4XRabK07v7SjgI0NHjYY9VTDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:35.028427Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.17779","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1937c1230054102b3f622fcb8b95ec7f3062a789d6925bb355172231e4376589","sha256:bffc7ef927fd3ef275731b815104d699f6a73db1fa73819df651be9b2e20a4c9"],"state_sha256":"f7b1ec96c1ac4a2c6c0bfd763ab212173a46d6f5819be4c63dfa1c3b1a9f2b7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bCxu/1lZM37/3l1VTZ1j0CMe7ao6Os+dILf8G7WVnJ/DWGUPD18S1BPsk+WWjHi5saa9JczYurW5f1Oj4BpWCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T01:58:24.404061Z","bundle_sha256":"0c2107c7cdbb39da1314b7901e9ed676043d82dcf7c2e6a8a2e2eedd5a93d8a1"}}