{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GSDNLCOOM2IG4SRKZFJ2ED5PLY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6db359cd1fd0fea28c031a37d1040df861e77fd2a1706aa27a3a83ba16a288ee","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-04T14:19:05Z","title_canon_sha256":"c1f0b2cf3630a9fad36178c1072ee738424c5ad7c7ceca3a81aed2a0d599afcf"},"schema_version":"1.0","source":{"id":"2502.02340","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.02340","created_at":"2026-07-05T10:09:27Z"},{"alias_kind":"arxiv_version","alias_value":"2502.02340v1","created_at":"2026-07-05T10:09:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02340","created_at":"2026-07-05T10:09:27Z"},{"alias_kind":"pith_short_12","alias_value":"GSDNLCOOM2IG","created_at":"2026-07-05T10:09:27Z"},{"alias_kind":"pith_short_16","alias_value":"GSDNLCOOM2IG4SRK","created_at":"2026-07-05T10:09:27Z"},{"alias_kind":"pith_short_8","alias_value":"GSDNLCOO","created_at":"2026-07-05T10:09:27Z"}],"graph_snapshots":[{"event_id":"sha256:fc7389a70b0fd27f7a5d7f9db9de59160b4738482cddab824373ab1b97b9b2fa","target":"graph","created_at":"2026-07-05T10:09:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.02340/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"How to mitigate negative transfer in transfer learning is a long-standing and challenging issue, especially in the application of medical image segmentation. Existing methods for reducing negative transfer focus on classification or regression tasks, ignoring the non-uniform negative transfer risk in different image regions. In this work, we propose a simple yet effective weighted fine-tuning method that directs the model's attention towards regions with significant transfer risk for medical semantic segmentation. Specifically, we compute a transferability-guided transfer risk map to quantify ","authors_text":"Guoqing Zhang, Jingyun Yang, Shutong Duan, Xiao-Ping Zhang, Yang Li, Yang Tan","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-04T14:19:05Z","title":"Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02340","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:12ca874e12a035ed184c7ef236c2fec426686d58df69791eb906a3d6b1edda5b","target":"record","created_at":"2026-07-05T10:09:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6db359cd1fd0fea28c031a37d1040df861e77fd2a1706aa27a3a83ba16a288ee","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-04T14:19:05Z","title_canon_sha256":"c1f0b2cf3630a9fad36178c1072ee738424c5ad7c7ceca3a81aed2a0d599afcf"},"schema_version":"1.0","source":{"id":"2502.02340","kind":"arxiv","version":1}},"canonical_sha256":"3486d589ce66906e4a2ac953a20faf5e3a2de7357a41f43b98d540acde2e5486","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3486d589ce66906e4a2ac953a20faf5e3a2de7357a41f43b98d540acde2e5486","first_computed_at":"2026-07-05T10:09:27.112136Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:09:27.112136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B3N0wHPKklsdPhfDSc1o6bV4MDwJ6jIup7QyNJ/cxU8WkCsVZTt5x27FIIbWG5pL/cfc59VolzJaxN3c/+F2AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:09:27.112701Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.02340","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:12ca874e12a035ed184c7ef236c2fec426686d58df69791eb906a3d6b1edda5b","sha256:fc7389a70b0fd27f7a5d7f9db9de59160b4738482cddab824373ab1b97b9b2fa"],"state_sha256":"12472708d0fd222fbf9ba169ea5f785a8328053fbffb39493cfaa54f8953e168"}