{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:BMU7QC2OF6MAS56CYIM66JG4ZV","short_pith_number":"pith:BMU7QC2O","canonical_record":{"source":{"id":"2106.00638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-01T17:09:47Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"95a2c22935228831830de84cddf734a90cbe485eb60ecaefa408af50860f0ac0","abstract_canon_sha256":"af2df11d788ee93c328306014f76198107e59cb4a82aa219ce64265f478cb65f"},"schema_version":"1.0"},"canonical_sha256":"0b29f80b4e2f980977c2c219ef24dccd7ae65ac04e654917d53d7b23f1829011","source":{"kind":"arxiv","id":"2106.00638","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.00638","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"arxiv_version","alias_value":"2106.00638v1","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.00638","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"pith_short_12","alias_value":"BMU7QC2OF6MA","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"pith_short_16","alias_value":"BMU7QC2OF6MAS56C","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"pith_short_8","alias_value":"BMU7QC2O","created_at":"2026-07-05T02:48:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:BMU7QC2OF6MAS56CYIM66JG4ZV","target":"record","payload":{"canonical_record":{"source":{"id":"2106.00638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-01T17:09:47Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"95a2c22935228831830de84cddf734a90cbe485eb60ecaefa408af50860f0ac0","abstract_canon_sha256":"af2df11d788ee93c328306014f76198107e59cb4a82aa219ce64265f478cb65f"},"schema_version":"1.0"},"canonical_sha256":"0b29f80b4e2f980977c2c219ef24dccd7ae65ac04e654917d53d7b23f1829011","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:00.224520Z","signature_b64":"P1oQJinR8pIqB61Zxy9wXFGGF7Pq7swG6T65sgGf41N3wP8rEEthafDRVkaU5nA28fcfNfTBqm4oKJhNsa5SAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b29f80b4e2f980977c2c219ef24dccd7ae65ac04e654917d53d7b23f1829011","last_reissued_at":"2026-07-05T02:48:00.224089Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:00.224089Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.00638","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":"2026-07-05T02:48:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"99DtQkIbdwXEU3oY67waiWsj2MfrqgUjVBkFl8+/zzErq/23DUqSIU6IgczGfNNqR3pcgQ+LSQK0hnYOB1EGBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:51:47.096793Z"},"content_sha256":"0c97c9284abcdccce8a0e40d644026ef6dbea6aba7d64918931b3d42b0504450","schema_version":"1.0","event_id":"sha256:0c97c9284abcdccce8a0e40d644026ef6dbea6aba7d64918931b3d42b0504450"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:BMU7QC2OF6MAS56CYIM66JG4ZV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jindong Gu, Volker Tresp, Yinchong Yang, Zhiliang Wu","submitted_at":"2021-06-01T17:09:47Z","abstract_excerpt":"Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process. Furthermore, we adapt different pre-training methods to investigate their impacts on the proposed model. We apply our approach to Bone Age Prediction and Lesion Localization. In most cases, the proposed model shows better performance compar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.00638","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/2106.00638/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":"2026-07-05T02:48:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gy88Xl+jEcYpKA2lVD0/yTSujJxme4PsYQys4QIJftrDBgStFo6uyB8HFo77VuOaTLIAZVKMCBkAauDTR4u+Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:51:47.097611Z"},"content_sha256":"a5b6eac7a8beb6324638aa8641b21b7d6642b3bcf8c7d5ce9476a3757fab634b","schema_version":"1.0","event_id":"sha256:a5b6eac7a8beb6324638aa8641b21b7d6642b3bcf8c7d5ce9476a3757fab634b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BMU7QC2OF6MAS56CYIM66JG4ZV/bundle.json","state_url":"https://pith.science/pith/BMU7QC2OF6MAS56CYIM66JG4ZV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BMU7QC2OF6MAS56CYIM66JG4ZV/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":"2026-08-04T16:51:47Z","links":{"resolver":"https://pith.science/pith/BMU7QC2OF6MAS56CYIM66JG4ZV","bundle":"https://pith.science/pith/BMU7QC2OF6MAS56CYIM66JG4ZV/bundle.json","state":"https://pith.science/pith/BMU7QC2OF6MAS56CYIM66JG4ZV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BMU7QC2OF6MAS56CYIM66JG4ZV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:BMU7QC2OF6MAS56CYIM66JG4ZV","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":"af2df11d788ee93c328306014f76198107e59cb4a82aa219ce64265f478cb65f","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-01T17:09:47Z","title_canon_sha256":"95a2c22935228831830de84cddf734a90cbe485eb60ecaefa408af50860f0ac0"},"schema_version":"1.0","source":{"id":"2106.00638","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.00638","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"arxiv_version","alias_value":"2106.00638v1","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.00638","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"pith_short_12","alias_value":"BMU7QC2OF6MA","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"pith_short_16","alias_value":"BMU7QC2OF6MAS56C","created_at":"2026-07-05T02:48:00Z"},{"alias_kind":"pith_short_8","alias_value":"BMU7QC2O","created_at":"2026-07-05T02:48:00Z"}],"graph_snapshots":[{"event_id":"sha256:a5b6eac7a8beb6324638aa8641b21b7d6642b3bcf8c7d5ce9476a3757fab634b","target":"graph","created_at":"2026-07-05T02:48:00Z","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/2106.00638/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process. Furthermore, we adapt different pre-training methods to investigate their impacts on the proposed model. We apply our approach to Bone Age Prediction and Lesion Localization. In most cases, the proposed model shows better performance compar","authors_text":"Jindong Gu, Volker Tresp, Yinchong Yang, Zhiliang Wu","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-01T17:09:47Z","title":"Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.00638","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":"sha256:0c97c9284abcdccce8a0e40d644026ef6dbea6aba7d64918931b3d42b0504450","target":"record","created_at":"2026-07-05T02:48:00Z","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":"af2df11d788ee93c328306014f76198107e59cb4a82aa219ce64265f478cb65f","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-01T17:09:47Z","title_canon_sha256":"95a2c22935228831830de84cddf734a90cbe485eb60ecaefa408af50860f0ac0"},"schema_version":"1.0","source":{"id":"2106.00638","kind":"arxiv","version":1}},"canonical_sha256":"0b29f80b4e2f980977c2c219ef24dccd7ae65ac04e654917d53d7b23f1829011","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b29f80b4e2f980977c2c219ef24dccd7ae65ac04e654917d53d7b23f1829011","first_computed_at":"2026-07-05T02:48:00.224089Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:48:00.224089Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P1oQJinR8pIqB61Zxy9wXFGGF7Pq7swG6T65sgGf41N3wP8rEEthafDRVkaU5nA28fcfNfTBqm4oKJhNsa5SAA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:48:00.224520Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.00638","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c97c9284abcdccce8a0e40d644026ef6dbea6aba7d64918931b3d42b0504450","sha256:a5b6eac7a8beb6324638aa8641b21b7d6642b3bcf8c7d5ce9476a3757fab634b"],"state_sha256":"2a62c745d061f25f41541e9807b6313c1c6b5df69651304b17d5a4d2dd940ff4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WtfJEvTYKvoif+tU0nqCkgJoV8O4bIHEYkFFJq23VlIZrenWBZp51MBrYQlRowojy/+Eih5mepxaw6Gqsj3tBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T16:51:47.102671Z","bundle_sha256":"15fd4545e80d22f9235ac1a8080d35d8ffcd5d48304910624108b9738cfc0315"}}