{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:CDKSSETFFAMYHRKR4FLN434PMZ","short_pith_number":"pith:CDKSSETF","canonical_record":{"source":{"id":"1610.07086","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-22T18:41:06Z","cross_cats_sorted":[],"title_canon_sha256":"8691c8dc0ac749dfbd96852ced29143da736674d64ccca418e9895cb638e52e8","abstract_canon_sha256":"f6190e63e1af45ad2fc7e1b8e3910a3ffeaa138f49dc98e094d81bd40690f9e8"},"schema_version":"1.0"},"canonical_sha256":"10d5291265281983c551e156de6f8f66590bf9d1d694220d30cea923c31f8c69","source":{"kind":"arxiv","id":"1610.07086","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.07086","created_at":"2026-05-18T00:41:38Z"},{"alias_kind":"arxiv_version","alias_value":"1610.07086v3","created_at":"2026-05-18T00:41:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.07086","created_at":"2026-05-18T00:41:38Z"},{"alias_kind":"pith_short_12","alias_value":"CDKSSETFFAMY","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"CDKSSETFFAMYHRKR","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"CDKSSETF","created_at":"2026-05-18T12:30:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:CDKSSETFFAMYHRKR4FLN434PMZ","target":"record","payload":{"canonical_record":{"source":{"id":"1610.07086","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-22T18:41:06Z","cross_cats_sorted":[],"title_canon_sha256":"8691c8dc0ac749dfbd96852ced29143da736674d64ccca418e9895cb638e52e8","abstract_canon_sha256":"f6190e63e1af45ad2fc7e1b8e3910a3ffeaa138f49dc98e094d81bd40690f9e8"},"schema_version":"1.0"},"canonical_sha256":"10d5291265281983c551e156de6f8f66590bf9d1d694220d30cea923c31f8c69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:38.742946Z","signature_b64":"SuE/aVPnvjjpXS03RPrLIf5SDdwptWUZmARPKe51LUkFwGCH9py5tsPfBE+l8g1Tv5QN0A1ZCJgDevi4FSj0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10d5291265281983c551e156de6f8f66590bf9d1d694220d30cea923c31f8c69","last_reissued_at":"2026-05-18T00:41:38.742336Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:38.742336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.07086","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nOCVvDXTBEmyfaou284TlEaROeF1vd/QMD0ydfYaGuC+06reSBFRIa/7GYckGpR6nxbz8WlTbJBc1SNZ9wl5Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T12:31:29.415722Z"},"content_sha256":"58687868a451eafd5abc2c38b4bd5837aa37bcc829f59c191e6643acf4258995","schema_version":"1.0","event_id":"sha256:58687868a451eafd5abc2c38b4bd5837aa37bcc829f59c191e6643acf4258995"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:CDKSSETFFAMYHRKR4FLN434PMZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep image mining for diabetic retinopathy screening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"B\\'eatrice Cochener, Gwenol\\'e Quellec, Katia Charri\\`ere, Mathieu Lamard, Yassine Boudi","submitted_at":"2016-10-22T18:41:06Z","abstract_excerpt":"Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.07086","kind":"arxiv","version":3},"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":"2026-05-18T00:41:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KF5vfMNQuXjPIYUpk5ZRULHTX9gQCjgEaOr0PpnWQUVqzfZkDvwytciieUrFO6LkEDyOAhReBNLHdgbFQhaJDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T12:31:29.416093Z"},"content_sha256":"9ea487b01a7acb99ceb6b3f85d30e085a85801fe8a2189a939abf3fa63536ee4","schema_version":"1.0","event_id":"sha256:9ea487b01a7acb99ceb6b3f85d30e085a85801fe8a2189a939abf3fa63536ee4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CDKSSETFFAMYHRKR4FLN434PMZ/bundle.json","state_url":"https://pith.science/pith/CDKSSETFFAMYHRKR4FLN434PMZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CDKSSETFFAMYHRKR4FLN434PMZ/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":"2026-06-11T12:31:29Z","links":{"resolver":"https://pith.science/pith/CDKSSETFFAMYHRKR4FLN434PMZ","bundle":"https://pith.science/pith/CDKSSETFFAMYHRKR4FLN434PMZ/bundle.json","state":"https://pith.science/pith/CDKSSETFFAMYHRKR4FLN434PMZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CDKSSETFFAMYHRKR4FLN434PMZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:CDKSSETFFAMYHRKR4FLN434PMZ","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":"f6190e63e1af45ad2fc7e1b8e3910a3ffeaa138f49dc98e094d81bd40690f9e8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-22T18:41:06Z","title_canon_sha256":"8691c8dc0ac749dfbd96852ced29143da736674d64ccca418e9895cb638e52e8"},"schema_version":"1.0","source":{"id":"1610.07086","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.07086","created_at":"2026-05-18T00:41:38Z"},{"alias_kind":"arxiv_version","alias_value":"1610.07086v3","created_at":"2026-05-18T00:41:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.07086","created_at":"2026-05-18T00:41:38Z"},{"alias_kind":"pith_short_12","alias_value":"CDKSSETFFAMY","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"CDKSSETFFAMYHRKR","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"CDKSSETF","created_at":"2026-05-18T12:30:09Z"}],"graph_snapshots":[{"event_id":"sha256:9ea487b01a7acb99ceb6b3f85d30e085a85801fe8a2189a939abf3fa63536ee4","target":"graph","created_at":"2026-05-18T00:41:38Z","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":"Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predic","authors_text":"B\\'eatrice Cochener, Gwenol\\'e Quellec, Katia Charri\\`ere, Mathieu Lamard, Yassine Boudi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-22T18:41:06Z","title":"Deep image mining for diabetic retinopathy screening"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.07086","kind":"arxiv","version":3},"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":"sha256:58687868a451eafd5abc2c38b4bd5837aa37bcc829f59c191e6643acf4258995","target":"record","created_at":"2026-05-18T00:41:38Z","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":"f6190e63e1af45ad2fc7e1b8e3910a3ffeaa138f49dc98e094d81bd40690f9e8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-22T18:41:06Z","title_canon_sha256":"8691c8dc0ac749dfbd96852ced29143da736674d64ccca418e9895cb638e52e8"},"schema_version":"1.0","source":{"id":"1610.07086","kind":"arxiv","version":3}},"canonical_sha256":"10d5291265281983c551e156de6f8f66590bf9d1d694220d30cea923c31f8c69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10d5291265281983c551e156de6f8f66590bf9d1d694220d30cea923c31f8c69","first_computed_at":"2026-05-18T00:41:38.742336Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:38.742336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SuE/aVPnvjjpXS03RPrLIf5SDdwptWUZmARPKe51LUkFwGCH9py5tsPfBE+l8g1Tv5QN0A1ZCJgDevi4FSj0Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:38.742946Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.07086","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58687868a451eafd5abc2c38b4bd5837aa37bcc829f59c191e6643acf4258995","sha256:9ea487b01a7acb99ceb6b3f85d30e085a85801fe8a2189a939abf3fa63536ee4"],"state_sha256":"0d5e0c735411ff7a93ab15a1696e5de884d84f55fb7c2c687c88188f275662f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V0zE6k9pjqHtQOgm9vKsoL/hcpjSW5WDN606+Zm3Sp2+6rnx67AEsXVfK739xQBMS3Kcbpw18q75ud5qVb+ZBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T12:31:29.418199Z","bundle_sha256":"66d02713bc6684ad1e3a3785b49fce498e70d8a19f883cda2c0da8e97020e715"}}