{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TBFB2FBQJACKL6NN76XU4TZX2G","short_pith_number":"pith:TBFB2FBQ","schema_version":"1.0","canonical_sha256":"984a1d14304804a5f9adffaf4e4f37d1b15efb1ed24511018d5513fb2fd32b3a","source":{"kind":"arxiv","id":"2207.11512","version":4},"attestation_state":"computed","paper":{"title":"Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoyuan Li, Huihua Yang, Lemeng Wang, Songlin Yan, Weijin Xu, Wentao Liu","submitted_at":"2022-07-23T13:02:43Z","abstract_excerpt":"Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manually annotating organs from CT scans is time-consuming and labor-intensive. Semi-supervised learning has shown the potential to alleviate this challenge by learning from a large set of unlabeled images and limited labeled samples. In this work, we follow the self-training strategy and employ a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unl"},"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":"2207.11512","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-23T13:02:43Z","cross_cats_sorted":[],"title_canon_sha256":"bfd044006dd1e8e0f7267c3ae3a2db4841a13bc2615f2336c59bf11c11de80a0","abstract_canon_sha256":"4e8e6df27f1c4d3afb2204467d05452b74412f6ce3c3a6479b4df775dbaf4bc9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:07:49.566166Z","signature_b64":"RWtOsz1bahYYkWGweJxCkkwggJXZNvN/lxODmKSb6Z0MwOIqZ1A1Hpqw56+TYBg1sftETI0eZBF2D0grvSAeAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"984a1d14304804a5f9adffaf4e4f37d1b15efb1ed24511018d5513fb2fd32b3a","last_reissued_at":"2026-07-05T05:07:49.565749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:07:49.565749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoyuan Li, Huihua Yang, Lemeng Wang, Songlin Yan, Weijin Xu, Wentao Liu","submitted_at":"2022-07-23T13:02:43Z","abstract_excerpt":"Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manually annotating organs from CT scans is time-consuming and labor-intensive. Semi-supervised learning has shown the potential to alleviate this challenge by learning from a large set of unlabeled images and limited labeled samples. In this work, we follow the self-training strategy and employ a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.11512","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/2207.11512/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":"2207.11512","created_at":"2026-07-05T05:07:49.565807+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.11512v4","created_at":"2026-07-05T05:07:49.565807+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.11512","created_at":"2026-07-05T05:07:49.565807+00:00"},{"alias_kind":"pith_short_12","alias_value":"TBFB2FBQJACK","created_at":"2026-07-05T05:07:49.565807+00:00"},{"alias_kind":"pith_short_16","alias_value":"TBFB2FBQJACKL6NN","created_at":"2026-07-05T05:07:49.565807+00:00"},{"alias_kind":"pith_short_8","alias_value":"TBFB2FBQ","created_at":"2026-07-05T05:07:49.565807+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/TBFB2FBQJACKL6NN76XU4TZX2G","json":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G.json","graph_json":"https://pith.science/api/pith-number/TBFB2FBQJACKL6NN76XU4TZX2G/graph.json","events_json":"https://pith.science/api/pith-number/TBFB2FBQJACKL6NN76XU4TZX2G/events.json","paper":"https://pith.science/paper/TBFB2FBQ"},"agent_actions":{"view_html":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G","download_json":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G.json","view_paper":"https://pith.science/paper/TBFB2FBQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.11512&json=true","fetch_graph":"https://pith.science/api/pith-number/TBFB2FBQJACKL6NN76XU4TZX2G/graph.json","fetch_events":"https://pith.science/api/pith-number/TBFB2FBQJACKL6NN76XU4TZX2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G/action/storage_attestation","attest_author":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G/action/author_attestation","sign_citation":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G/action/citation_signature","submit_replication":"https://pith.science/pith/TBFB2FBQJACKL6NN76XU4TZX2G/action/replication_record"}},"created_at":"2026-07-05T05:07:49.565807+00:00","updated_at":"2026-07-05T05:07:49.565807+00:00"}