{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IOGMM4CYS5P5HR7UWFUM6BK3JK","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":"40af049e5114d1c52ab41e1adb3a2a8882c20f9214188b3da1c63b5a7e231f31","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-11T00:12:59Z","title_canon_sha256":"1ca131f4610a247ce6f9ce28584c1cca5d9107118fc8909cac120daa4e56dda6"},"schema_version":"1.0","source":{"id":"2509.09064","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09064","created_at":"2026-07-05T12:09:28Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09064v1","created_at":"2026-07-05T12:09:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09064","created_at":"2026-07-05T12:09:28Z"},{"alias_kind":"pith_short_12","alias_value":"IOGMM4CYS5P5","created_at":"2026-07-05T12:09:28Z"},{"alias_kind":"pith_short_16","alias_value":"IOGMM4CYS5P5HR7U","created_at":"2026-07-05T12:09:28Z"},{"alias_kind":"pith_short_8","alias_value":"IOGMM4CY","created_at":"2026-07-05T12:09:28Z"}],"graph_snapshots":[{"event_id":"sha256:cc82e7731c3e7faf180068c36f3bf9504d693361447148aff35d5233bcef6d91","target":"graph","created_at":"2026-07-05T12:09:28Z","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/2509.09064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding 3D medical image volumes is critical in the medical field, yet existing 3D medical convolution and transformer-based self-supervised learning (SSL) methods often lack deep semantic comprehension. Recent advancements in multimodal large language models (MLLMs) provide a promising approach to enhance image understanding through text descriptions. To leverage these 2D MLLMs for improved 3D medical image understanding, we propose Med3DInsight, a novel pretraining framework that integrates 3D image encoders with 2D MLLMs via a specially designed plane-slice-aware transformer module. A","authors_text":"Huping Ye, Qiuhui Chen, Xuancheng Yao, Yi Hong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-11T00:12:59Z","title":"Enhancing 3D Medical Image Understanding with Pretraining Aided by 2D Multimodal Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09064","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":"sha256:21b07b41c7d9c4261b3d22ecd9def8797c16dc1c7fa1b19330630150818ba31b","target":"record","created_at":"2026-07-05T12:09:28Z","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":"40af049e5114d1c52ab41e1adb3a2a8882c20f9214188b3da1c63b5a7e231f31","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-11T00:12:59Z","title_canon_sha256":"1ca131f4610a247ce6f9ce28584c1cca5d9107118fc8909cac120daa4e56dda6"},"schema_version":"1.0","source":{"id":"2509.09064","kind":"arxiv","version":1}},"canonical_sha256":"438cc67058975fd3c7f4b168cf055b4a9e720b86ae81adb19a0c974345f585e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"438cc67058975fd3c7f4b168cf055b4a9e720b86ae81adb19a0c974345f585e6","first_computed_at":"2026-07-05T12:09:28.769004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:28.769004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"icWYFWST+SZlZgsNT0mrWbZl241XGuMRaAYS40PuyEfAx7RoYPFPfKava3lA+4xVCDtCB/MzAITNCxg48LCfBg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:28.769522Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09064","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21b07b41c7d9c4261b3d22ecd9def8797c16dc1c7fa1b19330630150818ba31b","sha256:cc82e7731c3e7faf180068c36f3bf9504d693361447148aff35d5233bcef6d91"],"state_sha256":"096f5054bb7ccee423f051bf2cc5e833e757d819fe534456b4562644594a7c48"}