{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:525647H2ZPWWBUIR2AO2IUVVL3","short_pith_number":"pith:525647H2","schema_version":"1.0","canonical_sha256":"eebbee7cfacbed60d111d01da452b55ecbb6810b0ea139b202977e7e8796ef0c","source":{"kind":"arxiv","id":"2110.06411","version":2},"attestation_state":"computed","paper":{"title":"A Teacher-Student Framework with Fourier Augmentation for COVID-19 Infection Segmentation in CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Han Chen, Hanseok Ko, Murray Loew, Yifan Jiang","submitted_at":"2021-10-13T00:37:34Z","abstract_excerpt":"Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase due to the domain shift. In this paper, we propose a novel unsupervised m"},"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":"2110.06411","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-10-13T00:37:34Z","cross_cats_sorted":[],"title_canon_sha256":"49a44ccbb6e0c4c324262bc33e27665a69ec833709a11238b98522ef1bb0ae72","abstract_canon_sha256":"c299c10ca2192468886779166ec1d329d982bdc0f7960e059835b4b7e60bf26d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:00:59.591489Z","signature_b64":"/R8UCHBXHJpl6LK0pTkb5FMWPPAHAwfRyfIfosiVRF+lZiNVFHF2oAv9eGknBbvJbp6a8yJCqvGbfCmD1+aVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eebbee7cfacbed60d111d01da452b55ecbb6810b0ea139b202977e7e8796ef0c","last_reissued_at":"2026-07-05T05:00:59.591104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:00:59.591104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Teacher-Student Framework with Fourier Augmentation for COVID-19 Infection Segmentation in CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Han Chen, Hanseok Ko, Murray Loew, Yifan Jiang","submitted_at":"2021-10-13T00:37:34Z","abstract_excerpt":"Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase due to the domain shift. In this paper, we propose a novel unsupervised m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.06411","kind":"arxiv","version":2},"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/2110.06411/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":"2110.06411","created_at":"2026-07-05T05:00:59.591159+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.06411v2","created_at":"2026-07-05T05:00:59.591159+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.06411","created_at":"2026-07-05T05:00:59.591159+00:00"},{"alias_kind":"pith_short_12","alias_value":"525647H2ZPWW","created_at":"2026-07-05T05:00:59.591159+00:00"},{"alias_kind":"pith_short_16","alias_value":"525647H2ZPWWBUIR","created_at":"2026-07-05T05:00:59.591159+00:00"},{"alias_kind":"pith_short_8","alias_value":"525647H2","created_at":"2026-07-05T05:00:59.591159+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/525647H2ZPWWBUIR2AO2IUVVL3","json":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3.json","graph_json":"https://pith.science/api/pith-number/525647H2ZPWWBUIR2AO2IUVVL3/graph.json","events_json":"https://pith.science/api/pith-number/525647H2ZPWWBUIR2AO2IUVVL3/events.json","paper":"https://pith.science/paper/525647H2"},"agent_actions":{"view_html":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3","download_json":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3.json","view_paper":"https://pith.science/paper/525647H2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.06411&json=true","fetch_graph":"https://pith.science/api/pith-number/525647H2ZPWWBUIR2AO2IUVVL3/graph.json","fetch_events":"https://pith.science/api/pith-number/525647H2ZPWWBUIR2AO2IUVVL3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3/action/storage_attestation","attest_author":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3/action/author_attestation","sign_citation":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3/action/citation_signature","submit_replication":"https://pith.science/pith/525647H2ZPWWBUIR2AO2IUVVL3/action/replication_record"}},"created_at":"2026-07-05T05:00:59.591159+00:00","updated_at":"2026-07-05T05:00:59.591159+00:00"}