{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DSPAHLJYIOISE2U7JRBBHCACDB","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":"b708c1530a547510fc54daa5248e23dd7c6558094d7c6dd426d0e2a76a70df41","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-18T18:11:53Z","title_canon_sha256":"66d3052551b5482a67657cc4143d6ed01826838a97c1c9ed50aa3bc7ef3344e4"},"schema_version":"1.0","source":{"id":"2411.11799","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.11799","created_at":"2026-07-05T09:37:01Z"},{"alias_kind":"arxiv_version","alias_value":"2411.11799v1","created_at":"2026-07-05T09:37:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11799","created_at":"2026-07-05T09:37:01Z"},{"alias_kind":"pith_short_12","alias_value":"DSPAHLJYIOIS","created_at":"2026-07-05T09:37:01Z"},{"alias_kind":"pith_short_16","alias_value":"DSPAHLJYIOISE2U7","created_at":"2026-07-05T09:37:01Z"},{"alias_kind":"pith_short_8","alias_value":"DSPAHLJY","created_at":"2026-07-05T09:37:01Z"}],"graph_snapshots":[{"event_id":"sha256:098d59d548ce8c7eef13364700cb683d62b0e37ac1c1aec1c1037b93324406b2","target":"graph","created_at":"2026-07-05T09:37:01Z","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/2411.11799/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning. While deep learning methods, particularly Convolutional Neural Networks (CNNs) and Transformers, have significantly advanced fusion performance, some of the existing CNN-based methods fall short in capturing fine-grained multiscale and edge features, leading to suboptimal feature integration. Transformer-based models, on the other hand, are computationally intensive in both the tr","authors_text":"Farzad Khalvati, Jiayi Wang, Meng Zhou, Xiaolan Xu, Yuxuan Zhang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-18T18:11:53Z","title":"Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11799","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:cd7a11a49cd5cd0771a0ec5e3b4d5006fa2f49786003ec66036f152a09880ece","target":"record","created_at":"2026-07-05T09:37:01Z","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":"b708c1530a547510fc54daa5248e23dd7c6558094d7c6dd426d0e2a76a70df41","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-18T18:11:53Z","title_canon_sha256":"66d3052551b5482a67657cc4143d6ed01826838a97c1c9ed50aa3bc7ef3344e4"},"schema_version":"1.0","source":{"id":"2411.11799","kind":"arxiv","version":1}},"canonical_sha256":"1c9e03ad384391226a9f4c421388021864eccae7808067b677facbf15fd91617","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c9e03ad384391226a9f4c421388021864eccae7808067b677facbf15fd91617","first_computed_at":"2026-07-05T09:37:01.911380Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:37:01.911380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XEz0/oP+HwMKtoe4aHMALa615EFtQYfLU5SPrRBzZ7W6eXeyps1LKSsomGfvKcqkOHcj2RYDVyzKUT1IKMmKAg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:37:01.912156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.11799","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd7a11a49cd5cd0771a0ec5e3b4d5006fa2f49786003ec66036f152a09880ece","sha256:098d59d548ce8c7eef13364700cb683d62b0e37ac1c1aec1c1037b93324406b2"],"state_sha256":"e4ba030fb818021aaf51a24b18d050682c3c422fad14fb9126fcca386f34762e"}