{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TZNCQ5YULB5BQTSIH44QVNQLOF","short_pith_number":"pith:TZNCQ5YU","schema_version":"1.0","canonical_sha256":"9e5a287714587a184e483f390ab60b7161ffaf2486100a7ef515e9a2c3f5a28b","source":{"kind":"arxiv","id":"2407.16684","version":3},"attestation_state":"computed","paper":{"title":"AutoRG-Brain: Grounded Report Generation for Brain MRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Chaoyi Wu, Jiayu Lei, Lisong Dai, Weidi Xie, Xiaoman Zhang, Yanfeng Wang, Yanyong Zhang, Ya Zhang, Yuehua Li","submitted_at":"2024-07-23T17:50:00Z","abstract_excerpt":"Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. To address these challenges, we initiate a series of work on grounded Automatic Report Generation (AutoRG), starting from the brain MRI interpretation system, which supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized fin"},"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":"2407.16684","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-07-23T17:50:00Z","cross_cats_sorted":["cs.CV","q-bio.NC"],"title_canon_sha256":"6cceca01491555fc958096c43fd11a3347a391cc49ce9fe64ea9f656777646c2","abstract_canon_sha256":"daccd697c30c376aa0d26ff94b2da9f0ddf8fbda9299b82a1de4bc3a5a180052"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:54.252664Z","signature_b64":"wR9hSr6zZMYRg6h4h5ZohM0LOkiQKc04xkK5nJZkfwj4stmUQUGK7Sg4oRPzxL4xb046lSB4vHtHebYyu3q+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e5a287714587a184e483f390ab60b7161ffaf2486100a7ef515e9a2c3f5a28b","last_reissued_at":"2026-07-05T08:49:54.252196Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:54.252196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoRG-Brain: Grounded Report Generation for Brain MRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Chaoyi Wu, Jiayu Lei, Lisong Dai, Weidi Xie, Xiaoman Zhang, Yanfeng Wang, Yanyong Zhang, Ya Zhang, Yuehua Li","submitted_at":"2024-07-23T17:50:00Z","abstract_excerpt":"Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. To address these challenges, we initiate a series of work on grounded Automatic Report Generation (AutoRG), starting from the brain MRI interpretation system, which supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.16684","kind":"arxiv","version":3},"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/2407.16684/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":"2407.16684","created_at":"2026-07-05T08:49:54.252247+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.16684v3","created_at":"2026-07-05T08:49:54.252247+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.16684","created_at":"2026-07-05T08:49:54.252247+00:00"},{"alias_kind":"pith_short_12","alias_value":"TZNCQ5YULB5B","created_at":"2026-07-05T08:49:54.252247+00:00"},{"alias_kind":"pith_short_16","alias_value":"TZNCQ5YULB5BQTSI","created_at":"2026-07-05T08:49:54.252247+00:00"},{"alias_kind":"pith_short_8","alias_value":"TZNCQ5YU","created_at":"2026-07-05T08:49:54.252247+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20525","citing_title":"NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF","json":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF.json","graph_json":"https://pith.science/api/pith-number/TZNCQ5YULB5BQTSIH44QVNQLOF/graph.json","events_json":"https://pith.science/api/pith-number/TZNCQ5YULB5BQTSIH44QVNQLOF/events.json","paper":"https://pith.science/paper/TZNCQ5YU"},"agent_actions":{"view_html":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF","download_json":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF.json","view_paper":"https://pith.science/paper/TZNCQ5YU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.16684&json=true","fetch_graph":"https://pith.science/api/pith-number/TZNCQ5YULB5BQTSIH44QVNQLOF/graph.json","fetch_events":"https://pith.science/api/pith-number/TZNCQ5YULB5BQTSIH44QVNQLOF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF/action/storage_attestation","attest_author":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF/action/author_attestation","sign_citation":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF/action/citation_signature","submit_replication":"https://pith.science/pith/TZNCQ5YULB5BQTSIH44QVNQLOF/action/replication_record"}},"created_at":"2026-07-05T08:49:54.252247+00:00","updated_at":"2026-07-05T08:49:54.252247+00:00"}