{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6BEPCA72MTNQDX5QMXX2BILCUC","short_pith_number":"pith:6BEPCA72","schema_version":"1.0","canonical_sha256":"f048f103fa64db01dfb065efa0a162a0b3ddc5bed558e1e9d7b013ec9dd43ede","source":{"kind":"arxiv","id":"2507.02488","version":2},"attestation_state":"computed","paper":{"title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongxing Li, Libin Lan, Zunhui Xia","submitted_at":"2025-07-03T09:51:45Z","abstract_excerpt":"Medical image recognition serves as a key way to aid in clinical diagnosis, enabling more accurate and timely identification of diseases and abnormalities. Vision transformer-based approaches have proven effective in handling various medical recognition tasks. However, these methods encounter two primary challenges. First, they are often task-specific and architecture-tailored, limiting their general applicability. Second, they usually either adopt full attention to model long-range dependencies, resulting in high computational costs, or rely on handcrafted sparse attention, potentially leadin"},"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":"2507.02488","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-03T09:51:45Z","cross_cats_sorted":[],"title_canon_sha256":"4dec8ec976236b0f0b0368c55e29ca4a348d1199c04788c1e2183d459b0a259a","abstract_canon_sha256":"9569645d6dbf03c498d6524dc1d93617b4807a6d7f532313a08f5a4718053b73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:26.951448Z","signature_b64":"Z2hQfXdCz6LljQeZAlL7GbdIko3ewRoj/D5Qxn7jY0ptOu3IaXxGrL2FN6GrxQEz4S2I0X81TiTOAH4ad7jGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f048f103fa64db01dfb065efa0a162a0b3ddc5bed558e1e9d7b013ec9dd43ede","last_reissued_at":"2026-07-05T11:48:26.950915Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:26.950915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongxing Li, Libin Lan, Zunhui Xia","submitted_at":"2025-07-03T09:51:45Z","abstract_excerpt":"Medical image recognition serves as a key way to aid in clinical diagnosis, enabling more accurate and timely identification of diseases and abnormalities. Vision transformer-based approaches have proven effective in handling various medical recognition tasks. However, these methods encounter two primary challenges. First, they are often task-specific and architecture-tailored, limiting their general applicability. Second, they usually either adopt full attention to model long-range dependencies, resulting in high computational costs, or rely on handcrafted sparse attention, potentially leadin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02488","kind":"arxiv","version":2},"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/2507.02488/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":"2507.02488","created_at":"2026-07-05T11:48:26.950985+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.02488v2","created_at":"2026-07-05T11:48:26.950985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02488","created_at":"2026-07-05T11:48:26.950985+00:00"},{"alias_kind":"pith_short_12","alias_value":"6BEPCA72MTNQ","created_at":"2026-07-05T11:48:26.950985+00:00"},{"alias_kind":"pith_short_16","alias_value":"6BEPCA72MTNQDX5Q","created_at":"2026-07-05T11:48:26.950985+00:00"},{"alias_kind":"pith_short_8","alias_value":"6BEPCA72","created_at":"2026-07-05T11:48:26.950985+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.08868","citing_title":"MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC","json":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC.json","graph_json":"https://pith.science/api/pith-number/6BEPCA72MTNQDX5QMXX2BILCUC/graph.json","events_json":"https://pith.science/api/pith-number/6BEPCA72MTNQDX5QMXX2BILCUC/events.json","paper":"https://pith.science/paper/6BEPCA72"},"agent_actions":{"view_html":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC","download_json":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC.json","view_paper":"https://pith.science/paper/6BEPCA72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.02488&json=true","fetch_graph":"https://pith.science/api/pith-number/6BEPCA72MTNQDX5QMXX2BILCUC/graph.json","fetch_events":"https://pith.science/api/pith-number/6BEPCA72MTNQDX5QMXX2BILCUC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC/action/storage_attestation","attest_author":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC/action/author_attestation","sign_citation":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC/action/citation_signature","submit_replication":"https://pith.science/pith/6BEPCA72MTNQDX5QMXX2BILCUC/action/replication_record"}},"created_at":"2026-07-05T11:48:26.950985+00:00","updated_at":"2026-07-05T11:48:26.950985+00:00"}