{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5NGYOFA2VVJZR65GLFFXOEXEPF","short_pith_number":"pith:5NGYOFA2","schema_version":"1.0","canonical_sha256":"eb4d87141aad5398fba6594b7712e4795932f756c83c4e11bcacb8c88b03340e","source":{"kind":"arxiv","id":"1906.04151","version":3},"attestation_state":"computed","paper":{"title":"Patch Transformer for Multi-tagging Whole Slide Histopathology Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haofu Liao, Jiebo Luo, Ke Cheng, Matt Wilder, Viet-Duy Nguyen, Weijian Li","submitted_at":"2019-06-10T17:43:52Z","abstract_excerpt":"Automated whole slide image (WSI) tagging has become a growing demand due to the increasing volume and diversity of WSIs collected nowadays in histopathology. Various methods have been studied to classify WSIs with single tags but none of them focuses on labeling WSIs with multiple tags. To this end, we propose a novel end-to-end trainable deep neural network named Patch Transformer which can effectively predict multiple slide-level tags from WSI patches based on both the correlations and the uniqueness between the tags. Specifically, the proposed method learns patch characteristics considerin"},"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":"1906.04151","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-10T17:43:52Z","cross_cats_sorted":[],"title_canon_sha256":"fa9fda0cf3c53e63fe1fd2ab1c8dda8e49e606ff408269465ba9118513e73577","abstract_canon_sha256":"91c4edef4f4cbc7edeef14fdfe7a599235e846c162d4003c7910f50a68e2f7cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:30.255147Z","signature_b64":"nPxT7qiGWEUQs82jSuB9gjTS0ntfUAiyFEWUjObqg5W6riUjQIbprSt7+0zkeNLSnYHpNZgg4q+89vUrZVfqAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb4d87141aad5398fba6594b7712e4795932f756c83c4e11bcacb8c88b03340e","last_reissued_at":"2026-05-17T23:41:30.254459Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:30.254459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Patch Transformer for Multi-tagging Whole Slide Histopathology Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haofu Liao, Jiebo Luo, Ke Cheng, Matt Wilder, Viet-Duy Nguyen, Weijian Li","submitted_at":"2019-06-10T17:43:52Z","abstract_excerpt":"Automated whole slide image (WSI) tagging has become a growing demand due to the increasing volume and diversity of WSIs collected nowadays in histopathology. Various methods have been studied to classify WSIs with single tags but none of them focuses on labeling WSIs with multiple tags. To this end, we propose a novel end-to-end trainable deep neural network named Patch Transformer which can effectively predict multiple slide-level tags from WSI patches based on both the correlations and the uniqueness between the tags. Specifically, the proposed method learns patch characteristics considerin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04151","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":""},"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":"1906.04151","created_at":"2026-05-17T23:41:30.254588+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04151v3","created_at":"2026-05-17T23:41:30.254588+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04151","created_at":"2026-05-17T23:41:30.254588+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NGYOFA2VVJZ","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NGYOFA2VVJZR65G","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NGYOFA2","created_at":"2026-05-18T12:33:10.108867+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/5NGYOFA2VVJZR65GLFFXOEXEPF","json":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF.json","graph_json":"https://pith.science/api/pith-number/5NGYOFA2VVJZR65GLFFXOEXEPF/graph.json","events_json":"https://pith.science/api/pith-number/5NGYOFA2VVJZR65GLFFXOEXEPF/events.json","paper":"https://pith.science/paper/5NGYOFA2"},"agent_actions":{"view_html":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF","download_json":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF.json","view_paper":"https://pith.science/paper/5NGYOFA2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04151&json=true","fetch_graph":"https://pith.science/api/pith-number/5NGYOFA2VVJZR65GLFFXOEXEPF/graph.json","fetch_events":"https://pith.science/api/pith-number/5NGYOFA2VVJZR65GLFFXOEXEPF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF/action/storage_attestation","attest_author":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF/action/author_attestation","sign_citation":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF/action/citation_signature","submit_replication":"https://pith.science/pith/5NGYOFA2VVJZR65GLFFXOEXEPF/action/replication_record"}},"created_at":"2026-05-17T23:41:30.254588+00:00","updated_at":"2026-05-17T23:41:30.254588+00:00"}