{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2O3ZKAGEJ47U3MVFTLHUCWVJQY","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":"4d990b34cbf3ce18500cc27ead18400b59c415cdf0b59beba15b5758af29a9ef","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-08-16T16:09:23Z","title_canon_sha256":"4e6562df971f5cc852d011bbe3259151f6a36738b798b61ff7247671559e87d8"},"schema_version":"1.0","source":{"id":"2308.08465","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.08465","created_at":"2026-07-05T06:41:59Z"},{"alias_kind":"arxiv_version","alias_value":"2308.08465v1","created_at":"2026-07-05T06:41:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.08465","created_at":"2026-07-05T06:41:59Z"},{"alias_kind":"pith_short_12","alias_value":"2O3ZKAGEJ47U","created_at":"2026-07-05T06:41:59Z"},{"alias_kind":"pith_short_16","alias_value":"2O3ZKAGEJ47U3MVF","created_at":"2026-07-05T06:41:59Z"},{"alias_kind":"pith_short_8","alias_value":"2O3ZKAGE","created_at":"2026-07-05T06:41:59Z"}],"graph_snapshots":[{"event_id":"sha256:d08bc018c6cf314ae55ccfe23a9e6b01d65bc350d680785c697c05488b173739","target":"graph","created_at":"2026-07-05T06:41:59Z","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/2308.08465/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning a medical image segmentation model is an inherently ambiguous task, as uncertainties exist in both images (noise) and manual annotations (human errors and bias) used for model training. To build a trustworthy image segmentation model, it is important to not just evaluate its performance but also estimate the uncertainty of the model prediction. Most state-of-the-art image segmentation networks adopt a hierarchical encoder architecture, extracting image features at multiple resolution levels from fine to coarse. In this work, we leverage this hierarchical image representation and propo","authors_text":"Wenjia Bai, Xinyu Bai","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-08-16T16:09:23Z","title":"Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.08465","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":"sha256:85b508d60c3d0ad2d935302784ef3cc293de38e9659ca4986cd1dce768610ec6","target":"record","created_at":"2026-07-05T06:41:59Z","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":"4d990b34cbf3ce18500cc27ead18400b59c415cdf0b59beba15b5758af29a9ef","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-08-16T16:09:23Z","title_canon_sha256":"4e6562df971f5cc852d011bbe3259151f6a36738b798b61ff7247671559e87d8"},"schema_version":"1.0","source":{"id":"2308.08465","kind":"arxiv","version":1}},"canonical_sha256":"d3b79500c44f3f4db2a59acf415aa9860e6d9d8495b20e297de9d17159da331b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3b79500c44f3f4db2a59acf415aa9860e6d9d8495b20e297de9d17159da331b","first_computed_at":"2026-07-05T06:41:59.506361Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:41:59.506361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hl69M6VVBWmLE3EEYJ3eQGViESNt6M0OO9SBbtbfax29UQD0MwF4VvA/heNKmW1srXtorOaooLfYjo7O7jHfDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:41:59.506741Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.08465","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85b508d60c3d0ad2d935302784ef3cc293de38e9659ca4986cd1dce768610ec6","sha256:d08bc018c6cf314ae55ccfe23a9e6b01d65bc350d680785c697c05488b173739"],"state_sha256":"e885e61f8f0802eae41bd46574d9944b8cd6f8baea8c16cf86b9d7be5c881a48"}