{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UXNETKYD5BCPLFDLQ46ZLPJCJK","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":"26a275e574612a7c692016ebcad21b7509ab1b6ebb60347ea0581784561c8861","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-08-03T20:41:35Z","title_canon_sha256":"a328f3ed42aab5b0a57e170e800c8511539e143e155d930431bb0102c32b6925"},"schema_version":"1.0","source":{"id":"2408.08881","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08881","created_at":"2026-07-05T10:02:05Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08881v3","created_at":"2026-07-05T10:02:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08881","created_at":"2026-07-05T10:02:05Z"},{"alias_kind":"pith_short_12","alias_value":"UXNETKYD5BCP","created_at":"2026-07-05T10:02:05Z"},{"alias_kind":"pith_short_16","alias_value":"UXNETKYD5BCPLFDL","created_at":"2026-07-05T10:02:05Z"},{"alias_kind":"pith_short_8","alias_value":"UXNETKYD","created_at":"2026-07-05T10:02:05Z"}],"graph_snapshots":[{"event_id":"sha256:d44e193fcb1bd674ea456faf50d8a24626e845fcf211d2a45df494fc2d93a09c","target":"graph","created_at":"2026-07-05T10:02:05Z","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/2408.08881/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by","authors_text":"Hongtu Zhu, Peng Huang, Pu Huang, Shu Hu, Xiaoyu Liu, Xin Wang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-08-03T20:41:35Z","title":"Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08881","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":"sha256:0f5c712cf4c43d9b80ee68842f654b80c3faf251ffa750a87ed83f791dede2ac","target":"record","created_at":"2026-07-05T10:02:05Z","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":"26a275e574612a7c692016ebcad21b7509ab1b6ebb60347ea0581784561c8861","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-08-03T20:41:35Z","title_canon_sha256":"a328f3ed42aab5b0a57e170e800c8511539e143e155d930431bb0102c32b6925"},"schema_version":"1.0","source":{"id":"2408.08881","kind":"arxiv","version":3}},"canonical_sha256":"a5da49ab03e844f5946b873d95bd224a927ce3f08faa998d4e403495bb6caa13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5da49ab03e844f5946b873d95bd224a927ce3f08faa998d4e403495bb6caa13","first_computed_at":"2026-07-05T10:02:05.579356Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:02:05.579356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UO6PLT9l0vc4rUX3JkkQT+FI8O99/2h0+g4DDUdaJBZBIa6pvPjTiPs38SKk16DDoocC/6awqmJZiTqYneawBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:02:05.579780Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.08881","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f5c712cf4c43d9b80ee68842f654b80c3faf251ffa750a87ed83f791dede2ac","sha256:d44e193fcb1bd674ea456faf50d8a24626e845fcf211d2a45df494fc2d93a09c"],"state_sha256":"60b32e08b5449aa6f0f39f735e4ea8503918c9286dd6e541a104d2a1e651d7c4"}