{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DJJ6HTXQPF5BI36XYSYINWTEI2","short_pith_number":"pith:DJJ6HTXQ","schema_version":"1.0","canonical_sha256":"1a53e3cef0797a146fd7c4b086da6446bd79c0ca545cfe393ac190bd1244de4e","source":{"kind":"arxiv","id":"2305.16214","version":1},"attestation_state":"computed","paper":{"title":"Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunna Tian, Fan Yang, Heng Zhou, Ran Ran, Xin Li, Zhenxi Zhang, Zhicheng Jiao","submitted_at":"2023-05-25T16:22:04Z","abstract_excerpt":"Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cr"},"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":"2305.16214","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T16:22:04Z","cross_cats_sorted":[],"title_canon_sha256":"b481eba83c6870e3fbc7bffacd68369e485f8017d54894438f5fe110cb9f5171","abstract_canon_sha256":"67f61e749cf63148f1f240f5d43c84fad1da15221199abd5580639f0d0b27144"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:00.806658Z","signature_b64":"5nnHNphepY7gbhLP7MXyIl9hak5UwNiPz6NqibcAIzIKyRLkccekpdl8ELmFzbfJ8Jaf9OUsafJnN/UJfQRrBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a53e3cef0797a146fd7c4b086da6446bd79c0ca545cfe393ac190bd1244de4e","last_reissued_at":"2026-07-05T06:14:00.806325Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:00.806325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunna Tian, Fan Yang, Heng Zhou, Ran Ran, Xin Li, Zhenxi Zhang, Zhicheng Jiao","submitted_at":"2023-05-25T16:22:04Z","abstract_excerpt":"Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16214","kind":"arxiv","version":1},"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/2305.16214/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":"2305.16214","created_at":"2026-07-05T06:14:00.806380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.16214v1","created_at":"2026-07-05T06:14:00.806380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16214","created_at":"2026-07-05T06:14:00.806380+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJJ6HTXQPF5B","created_at":"2026-07-05T06:14:00.806380+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJJ6HTXQPF5BI36X","created_at":"2026-07-05T06:14:00.806380+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJJ6HTXQ","created_at":"2026-07-05T06:14:00.806380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07019","citing_title":"SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2","json":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2.json","graph_json":"https://pith.science/api/pith-number/DJJ6HTXQPF5BI36XYSYINWTEI2/graph.json","events_json":"https://pith.science/api/pith-number/DJJ6HTXQPF5BI36XYSYINWTEI2/events.json","paper":"https://pith.science/paper/DJJ6HTXQ"},"agent_actions":{"view_html":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2","download_json":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2.json","view_paper":"https://pith.science/paper/DJJ6HTXQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.16214&json=true","fetch_graph":"https://pith.science/api/pith-number/DJJ6HTXQPF5BI36XYSYINWTEI2/graph.json","fetch_events":"https://pith.science/api/pith-number/DJJ6HTXQPF5BI36XYSYINWTEI2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2/action/storage_attestation","attest_author":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2/action/author_attestation","sign_citation":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2/action/citation_signature","submit_replication":"https://pith.science/pith/DJJ6HTXQPF5BI36XYSYINWTEI2/action/replication_record"}},"created_at":"2026-07-05T06:14:00.806380+00:00","updated_at":"2026-07-05T06:14:00.806380+00:00"}