{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:INTXB5G3ANGVO7UVLMPL422YVO","short_pith_number":"pith:INTXB5G3","schema_version":"1.0","canonical_sha256":"436770f4db034d577e955b1ebe6b58abb3fded0e1db6b6dbe55fe9202ee8effa","source":{"kind":"arxiv","id":"2505.12418","version":1},"attestation_state":"computed","paper":{"title":"Mutual Evidential Deep Learning for Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Chi-Man Pun, Lijian Li, Wenpin Jiao, Yali Bi, Yuanpeng He, Zhi Jin","submitted_at":"2025-05-18T13:42:27Z","abstract_excerpt":"Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels. Due to the averaging nature of their pseudo-label integration strategy, they fail to explore the reliability of pseudo-labels from different sources. In this paper, we propose a mutual evidential deep learning (MEDL) framework that offers a potentially viable solution for pseudo-label generation in semi-supervised learning from two perspectives. First, we introduce networks with different architecture"},"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":"2505.12418","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-05-18T13:42:27Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"37dc41db29ce28aebb6cbd7c9b64a727bcac6a4738c89f6988f31368b86987ed","abstract_canon_sha256":"1039725a4a7c792355c9af66aa4f7b5f16fbb02a61765f4bd131daff053dc841"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:03.910481Z","signature_b64":"mbmkMOmnqx6zJZD5v2syA/Q+Mse3BxXnfhE1ND669D1LfupZv574LhGv2653GNwCiCNRxQL1FSAe8hD1Zl5sAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"436770f4db034d577e955b1ebe6b58abb3fded0e1db6b6dbe55fe9202ee8effa","last_reissued_at":"2026-07-05T11:05:03.910022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:03.910022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mutual Evidential Deep Learning for Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Chi-Man Pun, Lijian Li, Wenpin Jiao, Yali Bi, Yuanpeng He, Zhi Jin","submitted_at":"2025-05-18T13:42:27Z","abstract_excerpt":"Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels. Due to the averaging nature of their pseudo-label integration strategy, they fail to explore the reliability of pseudo-labels from different sources. In this paper, we propose a mutual evidential deep learning (MEDL) framework that offers a potentially viable solution for pseudo-label generation in semi-supervised learning from two perspectives. First, we introduce networks with different architecture"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12418","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/2505.12418/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":"2505.12418","created_at":"2026-07-05T11:05:03.910084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.12418v1","created_at":"2026-07-05T11:05:03.910084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12418","created_at":"2026-07-05T11:05:03.910084+00:00"},{"alias_kind":"pith_short_12","alias_value":"INTXB5G3ANGV","created_at":"2026-07-05T11:05:03.910084+00:00"},{"alias_kind":"pith_short_16","alias_value":"INTXB5G3ANGVO7UV","created_at":"2026-07-05T11:05:03.910084+00:00"},{"alias_kind":"pith_short_8","alias_value":"INTXB5G3","created_at":"2026-07-05T11:05:03.910084+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/INTXB5G3ANGVO7UVLMPL422YVO","json":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO.json","graph_json":"https://pith.science/api/pith-number/INTXB5G3ANGVO7UVLMPL422YVO/graph.json","events_json":"https://pith.science/api/pith-number/INTXB5G3ANGVO7UVLMPL422YVO/events.json","paper":"https://pith.science/paper/INTXB5G3"},"agent_actions":{"view_html":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO","download_json":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO.json","view_paper":"https://pith.science/paper/INTXB5G3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.12418&json=true","fetch_graph":"https://pith.science/api/pith-number/INTXB5G3ANGVO7UVLMPL422YVO/graph.json","fetch_events":"https://pith.science/api/pith-number/INTXB5G3ANGVO7UVLMPL422YVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO/action/storage_attestation","attest_author":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO/action/author_attestation","sign_citation":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO/action/citation_signature","submit_replication":"https://pith.science/pith/INTXB5G3ANGVO7UVLMPL422YVO/action/replication_record"}},"created_at":"2026-07-05T11:05:03.910084+00:00","updated_at":"2026-07-05T11:05:03.910084+00:00"}