{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FXTIMNRNN3XSJ62T6NJN6FSJCP","short_pith_number":"pith:FXTIMNRN","schema_version":"1.0","canonical_sha256":"2de686362d6eef24fb53f352df164913fbc21096b352df1800a57c6681aa4219","source":{"kind":"arxiv","id":"2206.14718","version":4},"attestation_state":"computed","paper":{"title":"LViT: Language meets Vision Transformer in Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dakai Jin, Dazhou Guo, Le Lu, Puyang Wang, Qingde Li, Qingqi Hong, You Zhang, Yunxiang Li, Zihan Li","submitted_at":"2022-06-29T15:36:02Z","abstract_excerpt":"Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo l"},"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":"2206.14718","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-29T15:36:02Z","cross_cats_sorted":[],"title_canon_sha256":"c6debe3e135c350390a2795d550687067b16a99a6c155f07a7af4990197d6329","abstract_canon_sha256":"fd3a735311dc8a6d2382d4699f1b005f14e61e2b3b3a34ef02bcf3e4efb5236a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:44.116868Z","signature_b64":"HFv/tRazBJeHMdLM0m6Ufl89Pj6OB2KUbGQ7822RVfCewhrN72H2NCW/FxBkZOblLkxnAqkVqovDj1FxLbksCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2de686362d6eef24fb53f352df164913fbc21096b352df1800a57c6681aa4219","last_reissued_at":"2026-07-05T06:24:44.116440Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:44.116440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LViT: Language meets Vision Transformer in Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dakai Jin, Dazhou Guo, Le Lu, Puyang Wang, Qingde Li, Qingqi Hong, You Zhang, Yunxiang Li, Zihan Li","submitted_at":"2022-06-29T15:36:02Z","abstract_excerpt":"Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.14718","kind":"arxiv","version":4},"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/2206.14718/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":"2206.14718","created_at":"2026-07-05T06:24:44.116506+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.14718v4","created_at":"2026-07-05T06:24:44.116506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.14718","created_at":"2026-07-05T06:24:44.116506+00:00"},{"alias_kind":"pith_short_12","alias_value":"FXTIMNRNN3XS","created_at":"2026-07-05T06:24:44.116506+00:00"},{"alias_kind":"pith_short_16","alias_value":"FXTIMNRNN3XSJ62T","created_at":"2026-07-05T06:24:44.116506+00:00"},{"alias_kind":"pith_short_8","alias_value":"FXTIMNRN","created_at":"2026-07-05T06:24:44.116506+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/FXTIMNRNN3XSJ62T6NJN6FSJCP","json":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP.json","graph_json":"https://pith.science/api/pith-number/FXTIMNRNN3XSJ62T6NJN6FSJCP/graph.json","events_json":"https://pith.science/api/pith-number/FXTIMNRNN3XSJ62T6NJN6FSJCP/events.json","paper":"https://pith.science/paper/FXTIMNRN"},"agent_actions":{"view_html":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP","download_json":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP.json","view_paper":"https://pith.science/paper/FXTIMNRN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.14718&json=true","fetch_graph":"https://pith.science/api/pith-number/FXTIMNRNN3XSJ62T6NJN6FSJCP/graph.json","fetch_events":"https://pith.science/api/pith-number/FXTIMNRNN3XSJ62T6NJN6FSJCP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP/action/storage_attestation","attest_author":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP/action/author_attestation","sign_citation":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP/action/citation_signature","submit_replication":"https://pith.science/pith/FXTIMNRNN3XSJ62T6NJN6FSJCP/action/replication_record"}},"created_at":"2026-07-05T06:24:44.116506+00:00","updated_at":"2026-07-05T06:24:44.116506+00:00"}