{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FBXXRSLULJSVIMFVDK6QHCRM7B","short_pith_number":"pith:FBXXRSLU","schema_version":"1.0","canonical_sha256":"286f78c9745a655430b51abd038a2cf872faf84ff9e5705f6899f081f2e7c119","source":{"kind":"arxiv","id":"2312.07128","version":5},"attestation_state":"computed","paper":{"title":"MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jing Xu","submitted_at":"2023-12-12T10:04:11Z","abstract_excerpt":"Although transformer is preferred in natural language processing, some studies has only been applied to the field of medical imaging in recent years. For its long-term dependency, the transformer is expected to contribute to unconventional convolution neural net conquer their inherent spatial induction bias. The lately suggested transformer-based segmentation method only uses the transformer as an auxiliary module to help encode the global context into a convolutional representation. How to optimally integrate self-attention with convolution has not been investigated in depth. To solve the pro"},"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":"2312.07128","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-12-12T10:04:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"6a379a353462fe5acccea2194cc0ef74cfd9fe1d968b5665c2b45fc8f41780ca","abstract_canon_sha256":"8916c6abfe10e0ffd7785635cf61ae1359322240ba40393598d5fdefd1c84b59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:25.474160Z","signature_b64":"6iHKHyT9PiyVi6GYkwkl/6AuY8GTQKZ+bU9VBV2+o5Y0K6aXOMCMtYO3+prsaEYbCAaQUV7YlaWqh0z6N6WQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"286f78c9745a655430b51abd038a2cf872faf84ff9e5705f6899f081f2e7c119","last_reissued_at":"2026-07-05T09:07:25.473684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:25.473684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jing Xu","submitted_at":"2023-12-12T10:04:11Z","abstract_excerpt":"Although transformer is preferred in natural language processing, some studies has only been applied to the field of medical imaging in recent years. For its long-term dependency, the transformer is expected to contribute to unconventional convolution neural net conquer their inherent spatial induction bias. The lately suggested transformer-based segmentation method only uses the transformer as an auxiliary module to help encode the global context into a convolutional representation. How to optimally integrate self-attention with convolution has not been investigated in depth. To solve the pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07128","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/2312.07128/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":"2312.07128","created_at":"2026-07-05T09:07:25.473743+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.07128v5","created_at":"2026-07-05T09:07:25.473743+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07128","created_at":"2026-07-05T09:07:25.473743+00:00"},{"alias_kind":"pith_short_12","alias_value":"FBXXRSLULJSV","created_at":"2026-07-05T09:07:25.473743+00:00"},{"alias_kind":"pith_short_16","alias_value":"FBXXRSLULJSVIMFV","created_at":"2026-07-05T09:07:25.473743+00:00"},{"alias_kind":"pith_short_8","alias_value":"FBXXRSLU","created_at":"2026-07-05T09:07:25.473743+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/FBXXRSLULJSVIMFVDK6QHCRM7B","json":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B.json","graph_json":"https://pith.science/api/pith-number/FBXXRSLULJSVIMFVDK6QHCRM7B/graph.json","events_json":"https://pith.science/api/pith-number/FBXXRSLULJSVIMFVDK6QHCRM7B/events.json","paper":"https://pith.science/paper/FBXXRSLU"},"agent_actions":{"view_html":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B","download_json":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B.json","view_paper":"https://pith.science/paper/FBXXRSLU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.07128&json=true","fetch_graph":"https://pith.science/api/pith-number/FBXXRSLULJSVIMFVDK6QHCRM7B/graph.json","fetch_events":"https://pith.science/api/pith-number/FBXXRSLULJSVIMFVDK6QHCRM7B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B/action/storage_attestation","attest_author":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B/action/author_attestation","sign_citation":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B/action/citation_signature","submit_replication":"https://pith.science/pith/FBXXRSLULJSVIMFVDK6QHCRM7B/action/replication_record"}},"created_at":"2026-07-05T09:07:25.473743+00:00","updated_at":"2026-07-05T09:07:25.473743+00:00"}