{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:REOBTL2AZPOS625NY3DAE2747K","short_pith_number":"pith:REOBTL2A","canonical_record":{"source":{"id":"2409.00924","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-02T03:40:07Z","cross_cats_sorted":[],"title_canon_sha256":"1762637d82d5d301fb635f0ae03efcab83e2d5a52ed44f18dd94dfb04cd88af0","abstract_canon_sha256":"dcd2664832653d03b01939a78a0c2c41f7a88c7e7745331a0920c2ae46ce9fc0"},"schema_version":"1.0"},"canonical_sha256":"891c19af40cbdd2f6badc6c6026bfcfa861e7c35feea1ec2eb8c5153fc5621d0","source":{"kind":"arxiv","id":"2409.00924","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.00924","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"arxiv_version","alias_value":"2409.00924v1","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00924","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"pith_short_12","alias_value":"REOBTL2AZPOS","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"pith_short_16","alias_value":"REOBTL2AZPOS625N","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"pith_short_8","alias_value":"REOBTL2A","created_at":"2026-07-05T09:01:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:REOBTL2AZPOS625NY3DAE2747K","target":"record","payload":{"canonical_record":{"source":{"id":"2409.00924","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-02T03:40:07Z","cross_cats_sorted":[],"title_canon_sha256":"1762637d82d5d301fb635f0ae03efcab83e2d5a52ed44f18dd94dfb04cd88af0","abstract_canon_sha256":"dcd2664832653d03b01939a78a0c2c41f7a88c7e7745331a0920c2ae46ce9fc0"},"schema_version":"1.0"},"canonical_sha256":"891c19af40cbdd2f6badc6c6026bfcfa861e7c35feea1ec2eb8c5153fc5621d0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:01:55.341951Z","signature_b64":"KkM5/yZd62IszVoiyFGGCRBVK3cHkXgiUZtW+kt+BneCNQFUA0DCs3s2zrUr4we0N2WMA44Anbb9pUsNuNA1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"891c19af40cbdd2f6badc6c6026bfcfa861e7c35feea1ec2eb8c5153fc5621d0","last_reissued_at":"2026-07-05T09:01:55.341495Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:01:55.341495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.00924","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":"2026-07-05T09:01:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W7erkSpdjgEPsOVDlmz9pcnnUNuE0clgCZVTYnKV+eYA4mJCjQjP6ABIv74yogjWHOW9G5VJW9Q05rRzlxxcAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:06:25.440185Z"},"content_sha256":"3fcd39ce3795909a087fc7d3bf7532e75208903ade54c069b3156d9f01139c41","schema_version":"1.0","event_id":"sha256:3fcd39ce3795909a087fc7d3bf7532e75208903ade54c069b3156d9f01139c41"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:REOBTL2AZPOS625NY3DAE2747K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huazhu Fu, Hu Chen, Kai Ren, Ke Zou, Linchao He, Mengting Luo, Meng Wang, Nan Zhou, Yidi Chen, Yi Zhang","submitted_at":"2024-09-02T03:40:07Z","abstract_excerpt":"The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to varying prompt types and locations poses challenges. This paper addresses these challenges by focusing on the development of reliable prompts that enhance MedSAM's accuracy. We introduce MedSAM-U, an uncertainty-guided framework designed to automatically refine multi-prompt inputs for more reliable and precise medical image segmentation. Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, crea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00924","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/2409.00924/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":"2026-07-05T09:01:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kc82iYrpYQSd+UJTN1qe0TltgLUfyCvCeaQdl/UgFvf8TpBQA/TNQeo3MRnIxxwMmfGVg5U3tGzz7v8QDxprCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:06:25.440660Z"},"content_sha256":"8126c70b602f1d58b8e28ea3f80eb50c00ef3187c2f74640881e848b2224e5ae","schema_version":"1.0","event_id":"sha256:8126c70b602f1d58b8e28ea3f80eb50c00ef3187c2f74640881e848b2224e5ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/REOBTL2AZPOS625NY3DAE2747K/bundle.json","state_url":"https://pith.science/pith/REOBTL2AZPOS625NY3DAE2747K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/REOBTL2AZPOS625NY3DAE2747K/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":"2026-08-07T17:06:25Z","links":{"resolver":"https://pith.science/pith/REOBTL2AZPOS625NY3DAE2747K","bundle":"https://pith.science/pith/REOBTL2AZPOS625NY3DAE2747K/bundle.json","state":"https://pith.science/pith/REOBTL2AZPOS625NY3DAE2747K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/REOBTL2AZPOS625NY3DAE2747K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:REOBTL2AZPOS625NY3DAE2747K","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":"dcd2664832653d03b01939a78a0c2c41f7a88c7e7745331a0920c2ae46ce9fc0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-02T03:40:07Z","title_canon_sha256":"1762637d82d5d301fb635f0ae03efcab83e2d5a52ed44f18dd94dfb04cd88af0"},"schema_version":"1.0","source":{"id":"2409.00924","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.00924","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"arxiv_version","alias_value":"2409.00924v1","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00924","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"pith_short_12","alias_value":"REOBTL2AZPOS","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"pith_short_16","alias_value":"REOBTL2AZPOS625N","created_at":"2026-07-05T09:01:55Z"},{"alias_kind":"pith_short_8","alias_value":"REOBTL2A","created_at":"2026-07-05T09:01:55Z"}],"graph_snapshots":[{"event_id":"sha256:8126c70b602f1d58b8e28ea3f80eb50c00ef3187c2f74640881e848b2224e5ae","target":"graph","created_at":"2026-07-05T09:01:55Z","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/2409.00924/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to varying prompt types and locations poses challenges. This paper addresses these challenges by focusing on the development of reliable prompts that enhance MedSAM's accuracy. We introduce MedSAM-U, an uncertainty-guided framework designed to automatically refine multi-prompt inputs for more reliable and precise medical image segmentation. Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, crea","authors_text":"Huazhu Fu, Hu Chen, Kai Ren, Ke Zou, Linchao He, Mengting Luo, Meng Wang, Nan Zhou, Yidi Chen, Yi Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-02T03:40:07Z","title":"MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00924","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":"sha256:3fcd39ce3795909a087fc7d3bf7532e75208903ade54c069b3156d9f01139c41","target":"record","created_at":"2026-07-05T09:01:55Z","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":"dcd2664832653d03b01939a78a0c2c41f7a88c7e7745331a0920c2ae46ce9fc0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-02T03:40:07Z","title_canon_sha256":"1762637d82d5d301fb635f0ae03efcab83e2d5a52ed44f18dd94dfb04cd88af0"},"schema_version":"1.0","source":{"id":"2409.00924","kind":"arxiv","version":1}},"canonical_sha256":"891c19af40cbdd2f6badc6c6026bfcfa861e7c35feea1ec2eb8c5153fc5621d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"891c19af40cbdd2f6badc6c6026bfcfa861e7c35feea1ec2eb8c5153fc5621d0","first_computed_at":"2026-07-05T09:01:55.341495Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:01:55.341495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KkM5/yZd62IszVoiyFGGCRBVK3cHkXgiUZtW+kt+BneCNQFUA0DCs3s2zrUr4we0N2WMA44Anbb9pUsNuNA1Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:01:55.341951Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.00924","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fcd39ce3795909a087fc7d3bf7532e75208903ade54c069b3156d9f01139c41","sha256:8126c70b602f1d58b8e28ea3f80eb50c00ef3187c2f74640881e848b2224e5ae"],"state_sha256":"d88e3efd5a44de353c860326ae326d0ccac6528f4b0be73c1d720c3fe50c68e8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aUFAAyO2zRl/03+Km83Q7pEquOLuWcU1WvK/l1oNjmqyR//OObq51mStLHgrMHHZLqC0pgj2aayi3oVcVwBAAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:06:25.445415Z","bundle_sha256":"0f20cc82fc1b997ddb2fab4f067d780dcd79bdfb2e3db9c8287185632317c8d3"}}