{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YSLIP4NWOATQTY3VXLUZIGFOMU","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":"a7d88516681a3fa1e0c67ce9e614ba469e3cddad4d12e7b2c578210f9b72b5f9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-27T17:46:13Z","title_canon_sha256":"5ebd6234eed736ee10b579770afb20e336a597cb013c4ffb271305ad3e6ba0a6"},"schema_version":"1.0","source":{"id":"2406.19364","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.19364","created_at":"2026-07-05T09:11:27Z"},{"alias_kind":"arxiv_version","alias_value":"2406.19364v3","created_at":"2026-07-05T09:11:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19364","created_at":"2026-07-05T09:11:27Z"},{"alias_kind":"pith_short_12","alias_value":"YSLIP4NWOATQ","created_at":"2026-07-05T09:11:27Z"},{"alias_kind":"pith_short_16","alias_value":"YSLIP4NWOATQTY3V","created_at":"2026-07-05T09:11:27Z"},{"alias_kind":"pith_short_8","alias_value":"YSLIP4NW","created_at":"2026-07-05T09:11:27Z"}],"graph_snapshots":[{"event_id":"sha256:3e1284163a8f3f3afc365909339a51254b83d8d51dd497cae619b070455aece0","target":"graph","created_at":"2026-07-05T09:11:27Z","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/2406.19364/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Weakly-supervised medical image segmentation is a challenging task that aims to reduce the annotation cost while keep the segmentation performance. In this paper, we present a novel framework, SimTxtSeg, that leverages simple text cues to generate high-quality pseudo-labels and study the cross-modal fusion in training segmentation models, simultaneously. Our contribution consists of two key components: an effective Textual-to-Visual Cue Converter that produces visual prompts from text prompts on medical images, and a text-guided segmentation model with Text-Vision Hybrid Attention that fuses t","authors_text":"Geng Chen, Tao Zhou, Yi Zhou, Yuxin Xie","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-27T17:46:13Z","title":"SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19364","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":"sha256:90c0f8170b9423317925235265e423468fb7a68b193aa3e5ce596b9fe73bbe50","target":"record","created_at":"2026-07-05T09:11:27Z","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":"a7d88516681a3fa1e0c67ce9e614ba469e3cddad4d12e7b2c578210f9b72b5f9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-27T17:46:13Z","title_canon_sha256":"5ebd6234eed736ee10b579770afb20e336a597cb013c4ffb271305ad3e6ba0a6"},"schema_version":"1.0","source":{"id":"2406.19364","kind":"arxiv","version":3}},"canonical_sha256":"c49687f1b6702709e375bae99418ae6520ba07b25d44df03e0b5c56be8450e7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c49687f1b6702709e375bae99418ae6520ba07b25d44df03e0b5c56be8450e7b","first_computed_at":"2026-07-05T09:11:27.357762Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:11:27.357762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DeAu5YJH0+jIUARCCXtczoQvhehaFt5Ps3MayEAvQfsmi5LY53kwuyZn6gKzWrrSqj4BxZf8JhkMms430WMUCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:11:27.358294Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.19364","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90c0f8170b9423317925235265e423468fb7a68b193aa3e5ce596b9fe73bbe50","sha256:3e1284163a8f3f3afc365909339a51254b83d8d51dd497cae619b070455aece0"],"state_sha256":"c2a8a9ce0e8ba77e1d64aea10d51f98685a810f531bd6abf2ef1049ba1493c69"}