{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:L6XED3J3MJV6DI5JGKNQ7GSAOU","short_pith_number":"pith:L6XED3J3","schema_version":"1.0","canonical_sha256":"5fae41ed3b626be1a3a9329b0f9a40752281a3a24a4a1b913cff57769b5529a0","source":{"kind":"arxiv","id":"1710.04934","version":2},"attestation_state":"computed","paper":{"title":"RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar, Srikrishna Varadarajan","submitted_at":"2017-10-13T14:14:39Z","abstract_excerpt":"We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sifts through 2D cross-sectional slices while paying close attention to potential hemorrhagic regions. Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level. We refer to our proposed approach as Recurrent Attention De"},"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":"1710.04934","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-10-13T14:14:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d3e53b4c588b3b4d03683128a57495d743b9eb80d1147aee35021c76b3c1cd58","abstract_canon_sha256":"9eb42674a00425ee083e7d2079b9c0de914fe1c696b7e89c0434284fc0eea725"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:49.158127Z","signature_b64":"0MVBWhSxq4KyrwJw7XYCtv/1XoWzn9HEdoLn9ixrvUspY9H+T8nfoP+zd7XofgJNfCiGy67ofb9ynpbelxzKCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fae41ed3b626be1a3a9329b0f9a40752281a3a24a4a1b913cff57769b5529a0","last_reissued_at":"2026-05-18T00:26:49.157502Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:49.157502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar, Srikrishna Varadarajan","submitted_at":"2017-10-13T14:14:39Z","abstract_excerpt":"We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sifts through 2D cross-sectional slices while paying close attention to potential hemorrhagic regions. Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level. We refer to our proposed approach as Recurrent Attention De"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04934","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":""},"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":"1710.04934","created_at":"2026-05-18T00:26:49.157599+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04934v2","created_at":"2026-05-18T00:26:49.157599+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04934","created_at":"2026-05-18T00:26:49.157599+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6XED3J3MJV6","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6XED3J3MJV6DI5J","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6XED3J3","created_at":"2026-05-18T12:31:28.150371+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/L6XED3J3MJV6DI5JGKNQ7GSAOU","json":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU.json","graph_json":"https://pith.science/api/pith-number/L6XED3J3MJV6DI5JGKNQ7GSAOU/graph.json","events_json":"https://pith.science/api/pith-number/L6XED3J3MJV6DI5JGKNQ7GSAOU/events.json","paper":"https://pith.science/paper/L6XED3J3"},"agent_actions":{"view_html":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU","download_json":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU.json","view_paper":"https://pith.science/paper/L6XED3J3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04934&json=true","fetch_graph":"https://pith.science/api/pith-number/L6XED3J3MJV6DI5JGKNQ7GSAOU/graph.json","fetch_events":"https://pith.science/api/pith-number/L6XED3J3MJV6DI5JGKNQ7GSAOU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU/action/storage_attestation","attest_author":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU/action/author_attestation","sign_citation":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU/action/citation_signature","submit_replication":"https://pith.science/pith/L6XED3J3MJV6DI5JGKNQ7GSAOU/action/replication_record"}},"created_at":"2026-05-18T00:26:49.157599+00:00","updated_at":"2026-05-18T00:26:49.157599+00:00"}