{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QOLJSUDGEZJ5DQ7DWDLJXYGJET","short_pith_number":"pith:QOLJSUDG","schema_version":"1.0","canonical_sha256":"83969950662653d1c3e3b0d69be0c924e3e8d565aed9b18d501762e86953590c","source":{"kind":"arxiv","id":"2607.16705","version":1},"attestation_state":"computed","paper":{"title":"OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chen Yi, Cong-xuan zhang, Huan-huan Shi, Qin Lu, Shao-feng Jiang, Zhen Chen, Zhe-yang Jing","submitted_at":"2026-07-18T08:39:40Z","abstract_excerpt":"Semi-supervised learning (SSL) is an effective solution for medical image segmentation with limited annotations. Existing SSL methods mainly rely on pseudo-labels generated by teacher-student supervision or cross-network consistency. However, these methods lack an explicit structural reference for judging pseudo-label quality. Low-quality pseudo-labels may lead to unreliable training, error accumulation and confirmation bias when processing unlabeled data with substantial appearance variations. To address this issue, we proposed OFD-Net, a teacher-free single-network framework for reliable sem"},"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":"2607.16705","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-18T08:39:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c8f0ec5f7415b0004decf18ed087f54fec15ba15d3836e084b28102ba8d3e735","abstract_canon_sha256":"427b73e77fddd87036c221aff970d26a40395ce46e1bd4ca88c0001476d4249c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:20:22.891015Z","signature_b64":"EjiH5Iwo2UiDe/+f8x4jQR76pisSQubHlrbNUu34+SjFFx8E5aYvE4410Qf7XRiLsUswnXGlG0o1asReRXe4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83969950662653d1c3e3b0d69be0c924e3e8d565aed9b18d501762e86953590c","last_reissued_at":"2026-07-21T01:20:22.890141Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:20:22.890141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chen Yi, Cong-xuan zhang, Huan-huan Shi, Qin Lu, Shao-feng Jiang, Zhen Chen, Zhe-yang Jing","submitted_at":"2026-07-18T08:39:40Z","abstract_excerpt":"Semi-supervised learning (SSL) is an effective solution for medical image segmentation with limited annotations. Existing SSL methods mainly rely on pseudo-labels generated by teacher-student supervision or cross-network consistency. However, these methods lack an explicit structural reference for judging pseudo-label quality. Low-quality pseudo-labels may lead to unreliable training, error accumulation and confirmation bias when processing unlabeled data with substantial appearance variations. To address this issue, we proposed OFD-Net, a teacher-free single-network framework for reliable sem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16705","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/2607.16705/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":"2607.16705","created_at":"2026-07-21T01:20:22.890604+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.16705v1","created_at":"2026-07-21T01:20:22.890604+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.16705","created_at":"2026-07-21T01:20:22.890604+00:00"},{"alias_kind":"pith_short_12","alias_value":"QOLJSUDGEZJ5","created_at":"2026-07-21T01:20:22.890604+00:00"},{"alias_kind":"pith_short_16","alias_value":"QOLJSUDGEZJ5DQ7D","created_at":"2026-07-21T01:20:22.890604+00:00"},{"alias_kind":"pith_short_8","alias_value":"QOLJSUDG","created_at":"2026-07-21T01:20:22.890604+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/QOLJSUDGEZJ5DQ7DWDLJXYGJET","json":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET.json","graph_json":"https://pith.science/api/pith-number/QOLJSUDGEZJ5DQ7DWDLJXYGJET/graph.json","events_json":"https://pith.science/api/pith-number/QOLJSUDGEZJ5DQ7DWDLJXYGJET/events.json","paper":"https://pith.science/paper/QOLJSUDG"},"agent_actions":{"view_html":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET","download_json":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET.json","view_paper":"https://pith.science/paper/QOLJSUDG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.16705&json=true","fetch_graph":"https://pith.science/api/pith-number/QOLJSUDGEZJ5DQ7DWDLJXYGJET/graph.json","fetch_events":"https://pith.science/api/pith-number/QOLJSUDGEZJ5DQ7DWDLJXYGJET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET/action/storage_attestation","attest_author":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET/action/author_attestation","sign_citation":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET/action/citation_signature","submit_replication":"https://pith.science/pith/QOLJSUDGEZJ5DQ7DWDLJXYGJET/action/replication_record"}},"created_at":"2026-07-21T01:20:22.890604+00:00","updated_at":"2026-07-21T01:20:22.890604+00:00"}