{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FBJOOLBZLOSHQRARPV7IXAHGY5","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":"a4845d30f1ac620e8502166221d03bae8bd62a4ba24986670bc48f9596d10caa","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-21T08:01:58Z","title_canon_sha256":"3022e49e141b642cfe27592bdeb6629d5668ac02a25ee63f74c051e8a44dd5b9"},"schema_version":"1.0","source":{"id":"2410.15744","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15744","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15744v2","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15744","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_12","alias_value":"FBJOOLBZLOSH","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_16","alias_value":"FBJOOLBZLOSHQRAR","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_8","alias_value":"FBJOOLBZ","created_at":"2026-07-05T10:22:08Z"}],"graph_snapshots":[{"event_id":"sha256:d0af1860d05c390235f5f97ef3a92b6c7fe53a839de258f575935dc134393142","target":"graph","created_at":"2026-07-05T10:22:08Z","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/2410.15744/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in medical vision-language pre-training models have driven significant progress in zero-shot disease recognition. However, transferring image-level knowledge to pixel-level tasks, such as lesion segmentation in 3D CT scans, remains a critical challenge. Due to the complexity and variability of pathological visual characteristics, existing methods struggle to align fine-grained lesion features not encountered during training with disease-related textual representations. In this paper, we present Malenia, a novel multi-scale lesion-level mask-attribute alignment framework, sp","authors_text":"Shaoting Zhang, Wenhui Lei, Xiaofan Zhang, Yankai Jiang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-21T08:01:58Z","title":"Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15744","kind":"arxiv","version":2},"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":"sha256:941126057050a79f534c1336f75e4b8581beb49c43a0ef0dcda77dc879f7c6cb","target":"record","created_at":"2026-07-05T10:22:08Z","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":"a4845d30f1ac620e8502166221d03bae8bd62a4ba24986670bc48f9596d10caa","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-21T08:01:58Z","title_canon_sha256":"3022e49e141b642cfe27592bdeb6629d5668ac02a25ee63f74c051e8a44dd5b9"},"schema_version":"1.0","source":{"id":"2410.15744","kind":"arxiv","version":2}},"canonical_sha256":"2852e72c395ba47844117d7e8b80e6c742500e96db22ba9039c28a6e5f165472","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2852e72c395ba47844117d7e8b80e6c742500e96db22ba9039c28a6e5f165472","first_computed_at":"2026-07-05T10:22:08.303849Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:08.303849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"npf+SNHzaVzvhyPjALX8A5/AaYmF2tmIiPcKv+UfUcdDTv4OTqvrT2LPRBXujbRYoWbqTYQfIWvUvNRpY4+MAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:08.304422Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.15744","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:941126057050a79f534c1336f75e4b8581beb49c43a0ef0dcda77dc879f7c6cb","sha256:d0af1860d05c390235f5f97ef3a92b6c7fe53a839de258f575935dc134393142"],"state_sha256":"8e01818e7b6d85a577183da7b43b239f5f12538fe1818883c25c41738dd84ac6"}