{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:SNW3US64QVLU2GJRKFKWMSVHFY","short_pith_number":"pith:SNW3US64","canonical_record":{"source":{"id":"2010.14290","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-10-27T13:46:29Z","cross_cats_sorted":[],"title_canon_sha256":"4c23d3098cd46ad4f3639c381ea8f3bbffc84355d622616803a89f184ceb33aa","abstract_canon_sha256":"f23d71afceb880d298f78476b0e842b645112ab2554bf0ee03bf6cf54cd857e2"},"schema_version":"1.0"},"canonical_sha256":"936dba4bdc85574d19315155664aa72e379d4e0f4de0599f2c8c04dbafe40ce6","source":{"kind":"arxiv","id":"2010.14290","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.14290","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"arxiv_version","alias_value":"2010.14290v1","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.14290","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"pith_short_12","alias_value":"SNW3US64QVLU","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"pith_short_16","alias_value":"SNW3US64QVLU2GJR","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"pith_short_8","alias_value":"SNW3US64","created_at":"2026-07-05T01:46:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:SNW3US64QVLU2GJRKFKWMSVHFY","target":"record","payload":{"canonical_record":{"source":{"id":"2010.14290","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-10-27T13:46:29Z","cross_cats_sorted":[],"title_canon_sha256":"4c23d3098cd46ad4f3639c381ea8f3bbffc84355d622616803a89f184ceb33aa","abstract_canon_sha256":"f23d71afceb880d298f78476b0e842b645112ab2554bf0ee03bf6cf54cd857e2"},"schema_version":"1.0"},"canonical_sha256":"936dba4bdc85574d19315155664aa72e379d4e0f4de0599f2c8c04dbafe40ce6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:46:33.574172Z","signature_b64":"OONXu+Miv1x1zm8AQ/cZsOYjb0fm8pUvtDvV0swLxXi0LB+Ma9SuFnoaSdtqn0QojwIxw3hmTbH7/AmKUsRRAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"936dba4bdc85574d19315155664aa72e379d4e0f4de0599f2c8c04dbafe40ce6","last_reissued_at":"2026-07-05T01:46:33.573740Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:46:33.573740Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.14290","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":"2026-07-05T01:46:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S3csxP32TIXHiKBW2NVArjZU4i2EV0g8O+bXp+e1kBwuSOlUo976shdsZPVmWuxp/fVD6tBkQm8+ilwpCyS5CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T10:25:17.756439Z"},"content_sha256":"81db6a72193f4c3d043e4df5ed59ccf310434ec829838b09b8b8bec5186aa70f","schema_version":"1.0","event_id":"sha256:81db6a72193f4c3d043e4df5ed59ccf310434ec829838b09b8b8bec5186aa70f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:SNW3US64QVLU2GJRKFKWMSVHFY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Post Training Uncertainty Calibration of Deep Networks For Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Axel-Jan Rousseau, Dirk Valkenborg, Jeroen Bertels, Matthew B. Blaschko, Thijs Becker","submitted_at":"2020-10-27T13:46:29Z","abstract_excerpt":"Neural networks for automated image segmentation are typically trained to achieve maximum accuracy, while less attention has been given to the calibration of their confidence scores. However, well-calibrated confidence scores provide valuable information towards the user. We investigate several post hoc calibration methods that are straightforward to implement, some of which are novel. They are compared to Monte Carlo (MC) dropout. They are applied to neural networks trained with cross-entropy (CE) and soft Dice (SD) losses on BraTS 2018 and ISLES 2018. Surprisingly, models trained on SD loss "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.14290","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/2010.14290/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":"2026-07-05T01:46:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n+Fk9iD0GQJ3W/EJOpeDFl/CLJZ3xqKW24JcgH0DaGeQSGwn6Q7E2QeHDS02fPHDv5nzIYYuHntBf18/39S/Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T10:25:17.757967Z"},"content_sha256":"eacc6cf47116b211cad599509384770eedbdb79a723070cc9fefbe3d42551020","schema_version":"1.0","event_id":"sha256:eacc6cf47116b211cad599509384770eedbdb79a723070cc9fefbe3d42551020"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SNW3US64QVLU2GJRKFKWMSVHFY/bundle.json","state_url":"https://pith.science/pith/SNW3US64QVLU2GJRKFKWMSVHFY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SNW3US64QVLU2GJRKFKWMSVHFY/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":"2026-08-01T10:25:17Z","links":{"resolver":"https://pith.science/pith/SNW3US64QVLU2GJRKFKWMSVHFY","bundle":"https://pith.science/pith/SNW3US64QVLU2GJRKFKWMSVHFY/bundle.json","state":"https://pith.science/pith/SNW3US64QVLU2GJRKFKWMSVHFY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SNW3US64QVLU2GJRKFKWMSVHFY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:SNW3US64QVLU2GJRKFKWMSVHFY","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":"f23d71afceb880d298f78476b0e842b645112ab2554bf0ee03bf6cf54cd857e2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-10-27T13:46:29Z","title_canon_sha256":"4c23d3098cd46ad4f3639c381ea8f3bbffc84355d622616803a89f184ceb33aa"},"schema_version":"1.0","source":{"id":"2010.14290","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.14290","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"arxiv_version","alias_value":"2010.14290v1","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.14290","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"pith_short_12","alias_value":"SNW3US64QVLU","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"pith_short_16","alias_value":"SNW3US64QVLU2GJR","created_at":"2026-07-05T01:46:33Z"},{"alias_kind":"pith_short_8","alias_value":"SNW3US64","created_at":"2026-07-05T01:46:33Z"}],"graph_snapshots":[{"event_id":"sha256:eacc6cf47116b211cad599509384770eedbdb79a723070cc9fefbe3d42551020","target":"graph","created_at":"2026-07-05T01:46:33Z","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/2010.14290/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural networks for automated image segmentation are typically trained to achieve maximum accuracy, while less attention has been given to the calibration of their confidence scores. However, well-calibrated confidence scores provide valuable information towards the user. We investigate several post hoc calibration methods that are straightforward to implement, some of which are novel. They are compared to Monte Carlo (MC) dropout. They are applied to neural networks trained with cross-entropy (CE) and soft Dice (SD) losses on BraTS 2018 and ISLES 2018. Surprisingly, models trained on SD loss ","authors_text":"Axel-Jan Rousseau, Dirk Valkenborg, Jeroen Bertels, Matthew B. Blaschko, Thijs Becker","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-10-27T13:46:29Z","title":"Post Training Uncertainty Calibration of Deep Networks For Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.14290","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":"sha256:81db6a72193f4c3d043e4df5ed59ccf310434ec829838b09b8b8bec5186aa70f","target":"record","created_at":"2026-07-05T01:46:33Z","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":"f23d71afceb880d298f78476b0e842b645112ab2554bf0ee03bf6cf54cd857e2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-10-27T13:46:29Z","title_canon_sha256":"4c23d3098cd46ad4f3639c381ea8f3bbffc84355d622616803a89f184ceb33aa"},"schema_version":"1.0","source":{"id":"2010.14290","kind":"arxiv","version":1}},"canonical_sha256":"936dba4bdc85574d19315155664aa72e379d4e0f4de0599f2c8c04dbafe40ce6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"936dba4bdc85574d19315155664aa72e379d4e0f4de0599f2c8c04dbafe40ce6","first_computed_at":"2026-07-05T01:46:33.573740Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:46:33.573740Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OONXu+Miv1x1zm8AQ/cZsOYjb0fm8pUvtDvV0swLxXi0LB+Ma9SuFnoaSdtqn0QojwIxw3hmTbH7/AmKUsRRAg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:46:33.574172Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.14290","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:81db6a72193f4c3d043e4df5ed59ccf310434ec829838b09b8b8bec5186aa70f","sha256:eacc6cf47116b211cad599509384770eedbdb79a723070cc9fefbe3d42551020"],"state_sha256":"0ba076590234138d0036849e8ed8669678276d087c82b4cf5abe4617bfba473a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vwFuJNFmji4iVvfE1XJ9iPMi65I+u2oVKcHIwf50QVf0vO5UEE3zWgh1V9ZLVZYjU7fGFWcVIuW7iz0NFX0rCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T10:25:17.766047Z","bundle_sha256":"bcaa03e6134cdf5ca3e3a586476fc7b6184dcaa8a9834f58cace13eb0be0393b"}}