{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:JR5MP5Y5GDVB7TYQ5Z4NED3JXK","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":"e50f0e73946903e221728025d4c429df6b882cbf97f67e7f6e84dab866409d9b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-10T21:22:22Z","title_canon_sha256":"2bc8d85b9591225aca833a35b35c4532bb5d98ec8d30d142cbca5168b81e26d4"},"schema_version":"1.0","source":{"id":"2311.06400","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06400","created_at":"2026-07-05T07:11:39Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06400v1","created_at":"2026-07-05T07:11:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06400","created_at":"2026-07-05T07:11:39Z"},{"alias_kind":"pith_short_12","alias_value":"JR5MP5Y5GDVB","created_at":"2026-07-05T07:11:39Z"},{"alias_kind":"pith_short_16","alias_value":"JR5MP5Y5GDVB7TYQ","created_at":"2026-07-05T07:11:39Z"},{"alias_kind":"pith_short_8","alias_value":"JR5MP5Y5","created_at":"2026-07-05T07:11:39Z"}],"graph_snapshots":[{"event_id":"sha256:bfb750cac5f78a4cbb740d1c4caa6dd0b73e21783081747c19a2d62165b49d38","target":"graph","created_at":"2026-07-05T07:11:39Z","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/2311.06400/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image segmentation has immense clinical applicability but remains a challenge despite advancements in deep learning. The Segment Anything Model (SAM) exhibits potential in this field, yet the requirement for expertise intervention and the domain gap between natural and medical images poses significant obstacles. This paper introduces a novel training-free evidential prompt generation method named EviPrompt to overcome these issues. The proposed method, built on the inherent similarities within medical images, requires only a single reference image-annotation pair, making it a training-","authors_text":"Aidong Men, Jiaqi Tang, Qingchao Chen, Yinsong Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-10T21:22:22Z","title":"EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06400","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":"sha256:95ee6b3e23618994328e52ef22f9fbcaa23ee6c88d6c341194196d2ac1040ce9","target":"record","created_at":"2026-07-05T07:11:39Z","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":"e50f0e73946903e221728025d4c429df6b882cbf97f67e7f6e84dab866409d9b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-10T21:22:22Z","title_canon_sha256":"2bc8d85b9591225aca833a35b35c4532bb5d98ec8d30d142cbca5168b81e26d4"},"schema_version":"1.0","source":{"id":"2311.06400","kind":"arxiv","version":1}},"canonical_sha256":"4c7ac7f71d30ea1fcf10ee78d20f69ba9e108833c35cb9b52dd17e068f1d8b98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c7ac7f71d30ea1fcf10ee78d20f69ba9e108833c35cb9b52dd17e068f1d8b98","first_computed_at":"2026-07-05T07:11:39.488583Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:11:39.488583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HN5h5bYCSMQfoon+9DH7wmH8c8AW+as9/wqiIBVVwCZBY/+KthAhroIscZUUeVCA2nhxDXnWac1oyvon+wwqCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:11:39.489078Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.06400","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95ee6b3e23618994328e52ef22f9fbcaa23ee6c88d6c341194196d2ac1040ce9","sha256:bfb750cac5f78a4cbb740d1c4caa6dd0b73e21783081747c19a2d62165b49d38"],"state_sha256":"ea61ec4f56466e563648dd319ff6b78566ac38ddea414ba38bcb0f4d8650f1c1"}