{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EXVKABVLDSWC3CT23EMUVSLSH3","short_pith_number":"pith:EXVKABVL","schema_version":"1.0","canonical_sha256":"25eaa006ab1cac2d8a7ad9194ac9723ef2acc74f23a01ea01814aa24de654be8","source":{"kind":"arxiv","id":"2507.16122","version":3},"attestation_state":"computed","paper":{"title":"MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Biomedical, Department of Computer Science, KC Santosh (AI Research Lab, Nand Kumar Yadav, Rodrigue Rizk, Sanford School of Medicine, SD, Translational Sciences, University of South Dakota, USA), Vermillion, William CW Chen","submitted_at":"2025-07-22T00:30:44Z","abstract_excerpt":"Accurate and efficient medical image segmentation is crucial but challenging due to anatomical variability and high computational demands on volumetric data. Recent hybrid CNN-Transformer architectures achieve state-of-the-art results but add significant complexity. In this paper, we propose MLRU++, a Multiscale Lightweight Residual UNETR++ architecture designed to balance segmentation accuracy and computational efficiency. It introduces two key innovations: a Lightweight Channel and Bottleneck Attention Module (LCBAM) that enhances contextual feature encoding with minimal overhead, and a Mult"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.16122","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-07-22T00:30:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"ec620c5143249a4eab5c78ab5a5300dfcf863f37c98b86914216c2eeb3339617","abstract_canon_sha256":"2824f644575a81482976ccd497ccf7c8fe93fbcb6ce58a9d8f129976a669cc8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:14.182424Z","signature_b64":"NPdc9r3Uqb4y7lIWn7qLhC9LkreNMwEkQLbvWGqnd524f/NOC2Dg5HG9i770t4UroAT7KmO2/QuJNN9ca9lzDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25eaa006ab1cac2d8a7ad9194ac9723ef2acc74f23a01ea01814aa24de654be8","last_reissued_at":"2026-07-05T11:43:14.181930Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:14.181930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Biomedical, Department of Computer Science, KC Santosh (AI Research Lab, Nand Kumar Yadav, Rodrigue Rizk, Sanford School of Medicine, SD, Translational Sciences, University of South Dakota, USA), Vermillion, William CW Chen","submitted_at":"2025-07-22T00:30:44Z","abstract_excerpt":"Accurate and efficient medical image segmentation is crucial but challenging due to anatomical variability and high computational demands on volumetric data. Recent hybrid CNN-Transformer architectures achieve state-of-the-art results but add significant complexity. In this paper, we propose MLRU++, a Multiscale Lightweight Residual UNETR++ architecture designed to balance segmentation accuracy and computational efficiency. It introduces two key innovations: a Lightweight Channel and Bottleneck Attention Module (LCBAM) that enhances contextual feature encoding with minimal overhead, and a Mult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16122","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.16122/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.16122","created_at":"2026-07-05T11:43:14.181995+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.16122v3","created_at":"2026-07-05T11:43:14.181995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16122","created_at":"2026-07-05T11:43:14.181995+00:00"},{"alias_kind":"pith_short_12","alias_value":"EXVKABVLDSWC","created_at":"2026-07-05T11:43:14.181995+00:00"},{"alias_kind":"pith_short_16","alias_value":"EXVKABVLDSWC3CT2","created_at":"2026-07-05T11:43:14.181995+00:00"},{"alias_kind":"pith_short_8","alias_value":"EXVKABVL","created_at":"2026-07-05T11:43:14.181995+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3","json":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3.json","graph_json":"https://pith.science/api/pith-number/EXVKABVLDSWC3CT23EMUVSLSH3/graph.json","events_json":"https://pith.science/api/pith-number/EXVKABVLDSWC3CT23EMUVSLSH3/events.json","paper":"https://pith.science/paper/EXVKABVL"},"agent_actions":{"view_html":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3","download_json":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3.json","view_paper":"https://pith.science/paper/EXVKABVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.16122&json=true","fetch_graph":"https://pith.science/api/pith-number/EXVKABVLDSWC3CT23EMUVSLSH3/graph.json","fetch_events":"https://pith.science/api/pith-number/EXVKABVLDSWC3CT23EMUVSLSH3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3/action/storage_attestation","attest_author":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3/action/author_attestation","sign_citation":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3/action/citation_signature","submit_replication":"https://pith.science/pith/EXVKABVLDSWC3CT23EMUVSLSH3/action/replication_record"}},"created_at":"2026-07-05T11:43:14.181995+00:00","updated_at":"2026-07-05T11:43:14.181995+00:00"}