{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:M7JXMVCWGYDEIJS4SBHT742ERW","short_pith_number":"pith:M7JXMVCW","schema_version":"1.0","canonical_sha256":"67d3765456360644265c904f3ff3448dab0efe804f83769fc5cdb5454f845798","source":{"kind":"arxiv","id":"2307.03942","version":1},"attestation_state":"computed","paper":{"title":"Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Kaixin Chen, Kongming Liang, Mengqiu Xu, Ming Wu, Yi Zhong","submitted_at":"2023-07-08T09:36:17Z","abstract_excerpt":"Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to th"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2307.03942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2023-07-08T09:36:17Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2fe61c2bd98bff6bcbcb6d3a026ea3908dd6374bdc6d3c13804088774301816b","abstract_canon_sha256":"71d3dbc701cc5609ab35aa5e2212db1b030be9fa8a243166820146172d37b04d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:29:06.012816Z","signature_b64":"HmV54yuDFaCnmuGikCPWK9xUd/gQox0qS5wrHO0yhhjYNIM82wu7M6ijRFVvWANyoD+9nKmn++chNpFbMj8QAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67d3765456360644265c904f3ff3448dab0efe804f83769fc5cdb5454f845798","last_reissued_at":"2026-07-05T06:29:06.012341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:29:06.012341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Kaixin Chen, Kongming Liang, Mengqiu Xu, Ming Wu, Yi Zhong","submitted_at":"2023-07-08T09:36:17Z","abstract_excerpt":"Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03942","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/2307.03942/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2307.03942","created_at":"2026-07-05T06:29:06.012401+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.03942v1","created_at":"2026-07-05T06:29:06.012401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03942","created_at":"2026-07-05T06:29:06.012401+00:00"},{"alias_kind":"pith_short_12","alias_value":"M7JXMVCWGYDE","created_at":"2026-07-05T06:29:06.012401+00:00"},{"alias_kind":"pith_short_16","alias_value":"M7JXMVCWGYDEIJS4","created_at":"2026-07-05T06:29:06.012401+00:00"},{"alias_kind":"pith_short_8","alias_value":"M7JXMVCW","created_at":"2026-07-05T06:29:06.012401+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW","json":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW.json","graph_json":"https://pith.science/api/pith-number/M7JXMVCWGYDEIJS4SBHT742ERW/graph.json","events_json":"https://pith.science/api/pith-number/M7JXMVCWGYDEIJS4SBHT742ERW/events.json","paper":"https://pith.science/paper/M7JXMVCW"},"agent_actions":{"view_html":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW","download_json":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW.json","view_paper":"https://pith.science/paper/M7JXMVCW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.03942&json=true","fetch_graph":"https://pith.science/api/pith-number/M7JXMVCWGYDEIJS4SBHT742ERW/graph.json","fetch_events":"https://pith.science/api/pith-number/M7JXMVCWGYDEIJS4SBHT742ERW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW/action/storage_attestation","attest_author":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW/action/author_attestation","sign_citation":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW/action/citation_signature","submit_replication":"https://pith.science/pith/M7JXMVCWGYDEIJS4SBHT742ERW/action/replication_record"}},"created_at":"2026-07-05T06:29:06.012401+00:00","updated_at":"2026-07-05T06:29:06.012401+00:00"}