{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:Y774IZ3LJXJRR2HPCEIPCFKJIB","short_pith_number":"pith:Y774IZ3L","schema_version":"1.0","canonical_sha256":"c7ffc4676b4dd318e8ef1110f1154940484348660a70fcceec1c30b5fe9303bb","source":{"kind":"arxiv","id":"2204.06779","version":1},"attestation_state":"computed","paper":{"title":"3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Cheng Jiang, Jianbo Chang, Jianhua Yao, Jianye Pang, Ming Feng, Renzhi Wang, Yihao Chen","submitted_at":"2022-04-14T06:32:12Z","abstract_excerpt":"Dense prediction in medical volume provides enriched guidance for clinical analysis. CNN backbones have met bottleneck due to lack of long-range dependencies and global context modeling power. Recent works proposed to combine vision transformer with CNN, due to its strong global capture ability and learning capability. However, most works are limited to simply applying pure transformer with several fatal flaws (i.e., lack of inductive bias, heavy computation and little consideration for 3D data). Therefore, designing an elegant and efficient vision transformer learner for dense prediction in m"},"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":"2204.06779","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-14T06:32:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5553a10f7181713f4f67c310a258697b96e16bc39a6f49e738019a13b3111d05","abstract_canon_sha256":"df808ca7a2efb127d29389e713b95567e7074b5cf64ffab5e3bcd1a2464ae9a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:14:46.023295Z","signature_b64":"hNyG1dsBks39lUHl2oAYclRsjf+Q25bp6pPazv6YiPuCkHaDLdIvqcoQ8O1bR/6WAQln/7uhjogCKeXXecFtCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7ffc4676b4dd318e8ef1110f1154940484348660a70fcceec1c30b5fe9303bb","last_reissued_at":"2026-07-05T04:14:46.022942Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:14:46.022942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Cheng Jiang, Jianbo Chang, Jianhua Yao, Jianye Pang, Ming Feng, Renzhi Wang, Yihao Chen","submitted_at":"2022-04-14T06:32:12Z","abstract_excerpt":"Dense prediction in medical volume provides enriched guidance for clinical analysis. CNN backbones have met bottleneck due to lack of long-range dependencies and global context modeling power. Recent works proposed to combine vision transformer with CNN, due to its strong global capture ability and learning capability. However, most works are limited to simply applying pure transformer with several fatal flaws (i.e., lack of inductive bias, heavy computation and little consideration for 3D data). Therefore, designing an elegant and efficient vision transformer learner for dense prediction in m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.06779","kind":"arxiv","version":1},"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/2204.06779/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":"2204.06779","created_at":"2026-07-05T04:14:46.022996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.06779v1","created_at":"2026-07-05T04:14:46.022996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.06779","created_at":"2026-07-05T04:14:46.022996+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y774IZ3LJXJR","created_at":"2026-07-05T04:14:46.022996+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y774IZ3LJXJRR2HP","created_at":"2026-07-05T04:14:46.022996+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y774IZ3L","created_at":"2026-07-05T04:14:46.022996+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/Y774IZ3LJXJRR2HPCEIPCFKJIB","json":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB.json","graph_json":"https://pith.science/api/pith-number/Y774IZ3LJXJRR2HPCEIPCFKJIB/graph.json","events_json":"https://pith.science/api/pith-number/Y774IZ3LJXJRR2HPCEIPCFKJIB/events.json","paper":"https://pith.science/paper/Y774IZ3L"},"agent_actions":{"view_html":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB","download_json":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB.json","view_paper":"https://pith.science/paper/Y774IZ3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.06779&json=true","fetch_graph":"https://pith.science/api/pith-number/Y774IZ3LJXJRR2HPCEIPCFKJIB/graph.json","fetch_events":"https://pith.science/api/pith-number/Y774IZ3LJXJRR2HPCEIPCFKJIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB/action/storage_attestation","attest_author":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB/action/author_attestation","sign_citation":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB/action/citation_signature","submit_replication":"https://pith.science/pith/Y774IZ3LJXJRR2HPCEIPCFKJIB/action/replication_record"}},"created_at":"2026-07-05T04:14:46.022996+00:00","updated_at":"2026-07-05T04:14:46.022996+00:00"}