{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NJ2HSUGZ3HMEKPKS2W2J3UUGXC","short_pith_number":"pith:NJ2HSUGZ","schema_version":"1.0","canonical_sha256":"6a747950d9d9d8453d52d5b49dd286b883b30cd3d4e98a1bba5da8327ee5859a","source":{"kind":"arxiv","id":"2203.00131","version":5},"attestation_state":"computed","paper":{"title":"A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Di Liu, Dimitris N. Metaxas, Mu Zhou, Shaoting Zhang, Yunhe Gao, Zhennan Yan","submitted_at":"2022-02-28T22:59:42Z","abstract_excerpt":"Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medical data and are unable to generalize on diverse medical image tasks. To tackle these challenges, we present MedFormer, a data-scalable Transformer designed for generalizable 3D medical image segmentation. Our approach incorporates three key elements: a desirable inductive bias, hierarchical modeling with linear-complexity attention, and multi-scale feature fusion that integrates spatial and semantic information globa"},"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":"2203.00131","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-02-28T22:59:42Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d5d5c2675612422ebed8e5043c52ea93fc233870123d74d6987fdc3a6349f50e","abstract_canon_sha256":"acc1f63f60ecf0438f9e3f99c31ba6e035751821167c316ae66c84b4b549bcee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:58:15.195444Z","signature_b64":"blyBDTeO70zzT5YI30xevGpE+ZvkeRRQ5ExBLoeiwPzLodFM/thdGy+64MuyPmKhqApfQ33KsX16OFehGBFlCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a747950d9d9d8453d52d5b49dd286b883b30cd3d4e98a1bba5da8327ee5859a","last_reissued_at":"2026-07-05T05:58:15.195079Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:58:15.195079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Di Liu, Dimitris N. Metaxas, Mu Zhou, Shaoting Zhang, Yunhe Gao, Zhennan Yan","submitted_at":"2022-02-28T22:59:42Z","abstract_excerpt":"Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medical data and are unable to generalize on diverse medical image tasks. To tackle these challenges, we present MedFormer, a data-scalable Transformer designed for generalizable 3D medical image segmentation. Our approach incorporates three key elements: a desirable inductive bias, hierarchical modeling with linear-complexity attention, and multi-scale feature fusion that integrates spatial and semantic information globa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.00131","kind":"arxiv","version":5},"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/2203.00131/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":"2203.00131","created_at":"2026-07-05T05:58:15.195135+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.00131v5","created_at":"2026-07-05T05:58:15.195135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.00131","created_at":"2026-07-05T05:58:15.195135+00:00"},{"alias_kind":"pith_short_12","alias_value":"NJ2HSUGZ3HME","created_at":"2026-07-05T05:58:15.195135+00:00"},{"alias_kind":"pith_short_16","alias_value":"NJ2HSUGZ3HMEKPKS","created_at":"2026-07-05T05:58:15.195135+00:00"},{"alias_kind":"pith_short_8","alias_value":"NJ2HSUGZ","created_at":"2026-07-05T05:58:15.195135+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2401.02458","citing_title":"Data-Centric Foundation Models in Computational Healthcare: A Survey","ref_index":90,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22619","citing_title":"GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2601.19690","citing_title":"DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17208","citing_title":"CDSA-Net:Collaborative Decoupling of Vascular Structure and Background for High-Fidelity Coronary Digital Subtraction Angiography","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20286","citing_title":"MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC","json":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC.json","graph_json":"https://pith.science/api/pith-number/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/graph.json","events_json":"https://pith.science/api/pith-number/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/events.json","paper":"https://pith.science/paper/NJ2HSUGZ"},"agent_actions":{"view_html":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC","download_json":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC.json","view_paper":"https://pith.science/paper/NJ2HSUGZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.00131&json=true","fetch_graph":"https://pith.science/api/pith-number/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/graph.json","fetch_events":"https://pith.science/api/pith-number/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/action/storage_attestation","attest_author":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/action/author_attestation","sign_citation":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/action/citation_signature","submit_replication":"https://pith.science/pith/NJ2HSUGZ3HMEKPKS2W2J3UUGXC/action/replication_record"}},"created_at":"2026-07-05T05:58:15.195135+00:00","updated_at":"2026-07-05T05:58:15.195135+00:00"}