{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GU6IKFFNU5NKMCVFXRC5XXDNLM","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":"0bd3c1194c963a9eb8abcde9cea3f2b409e3be31d1d09f0c4483cecdf76a9b53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-07T14:47:15Z","title_canon_sha256":"bb9c99e6cb0ed2d32998ae66c57e4d61f9387d953d552c00ac4aa053d1107235"},"schema_version":"1.0","source":{"id":"2501.03838","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.03838","created_at":"2026-07-05T09:58:01Z"},{"alias_kind":"arxiv_version","alias_value":"2501.03838v1","created_at":"2026-07-05T09:58:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03838","created_at":"2026-07-05T09:58:01Z"},{"alias_kind":"pith_short_12","alias_value":"GU6IKFFNU5NK","created_at":"2026-07-05T09:58:01Z"},{"alias_kind":"pith_short_16","alias_value":"GU6IKFFNU5NKMCVF","created_at":"2026-07-05T09:58:01Z"},{"alias_kind":"pith_short_8","alias_value":"GU6IKFFN","created_at":"2026-07-05T09:58:01Z"}],"graph_snapshots":[{"event_id":"sha256:a5f196f5cac844f714544c26ab86bf842ec6a6ef1bab5f8a6120564d29127446","target":"graph","created_at":"2026-07-05T09:58:01Z","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/2501.03838/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current medical image segmentation approaches have limitations in deeply exploring multi-scale information and effectively combining local detail textures with global contextual semantic information. This results in over-segmentation, under-segmentation, and blurred segmentation boundaries. To tackle these challenges, we explore multi-scale feature representations from different perspectives, proposing a novel, lightweight, and multi-scale architecture (LM-Net) that integrates advantages of both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance segmentation accurac","authors_text":"Chaoyin She, Qinghua Huang, Wei Wang, Zhenkun Lu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-07T14:47:15Z","title":"LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03838","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":"sha256:298bd8ff00dfac2e9c3347c2a7d910b28a04f7c78ad5232c05e28750c50ef638","target":"record","created_at":"2026-07-05T09:58:01Z","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":"0bd3c1194c963a9eb8abcde9cea3f2b409e3be31d1d09f0c4483cecdf76a9b53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-07T14:47:15Z","title_canon_sha256":"bb9c99e6cb0ed2d32998ae66c57e4d61f9387d953d552c00ac4aa053d1107235"},"schema_version":"1.0","source":{"id":"2501.03838","kind":"arxiv","version":1}},"canonical_sha256":"353c8514ada75aa60aa5bc45dbdc6d5b062a359a2a23dbd6a2cc78853d58181b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"353c8514ada75aa60aa5bc45dbdc6d5b062a359a2a23dbd6a2cc78853d58181b","first_computed_at":"2026-07-05T09:58:01.101761Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:58:01.101761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XmmDLZJgEaqSLfOjL6h/zZVTNYykIoFqesEVq3vQMU+ne/6K8NONOuOv9OPeMKrSNHr/y85IHEkDAuG66Ya9BA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:58:01.102190Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.03838","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:298bd8ff00dfac2e9c3347c2a7d910b28a04f7c78ad5232c05e28750c50ef638","sha256:a5f196f5cac844f714544c26ab86bf842ec6a6ef1bab5f8a6120564d29127446"],"state_sha256":"19388090ddc560e7fe2b120ab3c616967e0627dae42ad56ed95199555606162b"}