{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:L2X47SKOCZU3RNEWEWHSBHLMKK","short_pith_number":"pith:L2X47SKO","schema_version":"1.0","canonical_sha256":"5eafcfc94e1669b8b496258f209d6c528ad64d41f0933e928fa13996b7dbf258","source":{"kind":"arxiv","id":"2003.13865","version":3},"attestation_state":"computed","paper":{"title":"COVID-CT-Dataset: A CT Scan Dataset about COVID-19","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinyu Zhao, Pengtao Xie, Shanghang Zhang, Xingyi Yang, Xuehai He, Yichen Zhang","submitted_at":"2020-03-30T23:27:24Z","abstract_excerpt":"During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbrea"},"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":"2003.13865","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-30T23:27:24Z","cross_cats_sorted":["cs.CV","eess.IV","stat.ML"],"title_canon_sha256":"f6faae1dd15079a4384a3adce8b5a6e9136c286c07868001f03bfc37b402ba32","abstract_canon_sha256":"732a50dacc696ed168e681e4f88f3c60fc46bcc7e0436430b12362a6951483c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:13.269960Z","signature_b64":"yB2pxjhIJqdV+kEcpKq3HC58U/CvdVnl6FUDKwGAWuJJp3I/z0MjyKoCImhsf+T5iv+ndt0E5H1/eBOLHSoeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5eafcfc94e1669b8b496258f209d6c528ad64d41f0933e928fa13996b7dbf258","last_reissued_at":"2026-07-05T01:11:13.269448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:13.269448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COVID-CT-Dataset: A CT Scan Dataset about COVID-19","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinyu Zhao, Pengtao Xie, Shanghang Zhang, Xingyi Yang, Xuehai He, Yichen Zhang","submitted_at":"2020-03-30T23:27:24Z","abstract_excerpt":"During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbrea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.13865","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/2003.13865/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":"2003.13865","created_at":"2026-07-05T01:11:13.269519+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.13865v3","created_at":"2026-07-05T01:11:13.269519+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.13865","created_at":"2026-07-05T01:11:13.269519+00:00"},{"alias_kind":"pith_short_12","alias_value":"L2X47SKOCZU3","created_at":"2026-07-05T01:11:13.269519+00:00"},{"alias_kind":"pith_short_16","alias_value":"L2X47SKOCZU3RNEW","created_at":"2026-07-05T01:11:13.269519+00:00"},{"alias_kind":"pith_short_8","alias_value":"L2X47SKO","created_at":"2026-07-05T01:11:13.269519+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26287","citing_title":"A multifractal-based masked auto-encoder: an application to medical images","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08333","citing_title":"Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13756","citing_title":"MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging","ref_index":80,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04447","citing_title":"Deep Reprogramming Distillation for Medical Foundation Models","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK","json":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK.json","graph_json":"https://pith.science/api/pith-number/L2X47SKOCZU3RNEWEWHSBHLMKK/graph.json","events_json":"https://pith.science/api/pith-number/L2X47SKOCZU3RNEWEWHSBHLMKK/events.json","paper":"https://pith.science/paper/L2X47SKO"},"agent_actions":{"view_html":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK","download_json":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK.json","view_paper":"https://pith.science/paper/L2X47SKO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.13865&json=true","fetch_graph":"https://pith.science/api/pith-number/L2X47SKOCZU3RNEWEWHSBHLMKK/graph.json","fetch_events":"https://pith.science/api/pith-number/L2X47SKOCZU3RNEWEWHSBHLMKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK/action/storage_attestation","attest_author":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK/action/author_attestation","sign_citation":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK/action/citation_signature","submit_replication":"https://pith.science/pith/L2X47SKOCZU3RNEWEWHSBHLMKK/action/replication_record"}},"created_at":"2026-07-05T01:11:13.269519+00:00","updated_at":"2026-07-05T01:11:13.269519+00:00"}