{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OAN64JZSA46DKZFHJI2ZDC66VE","short_pith_number":"pith:OAN64JZS","schema_version":"1.0","canonical_sha256":"701bee2732073c3564a74a35918bdea909f7dab7ae6667946820fb6b3d88ec70","source":{"kind":"arxiv","id":"1712.03747","version":3},"attestation_state":"computed","paper":{"title":"Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arnau Oliver, Daniel S. Asfaw, Jose Bernal, Kaisar Kushibar, Robert Mart\\'i, Sergi Valverde, Xavier Llad\\'o","submitted_at":"2017-12-11T12:25:30Z","abstract_excerpt":"In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of thi"},"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":"1712.03747","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-11T12:25:30Z","cross_cats_sorted":[],"title_canon_sha256":"aa4c0ff3531a45769874b7977396023226b8e53ca4d4b69fef29f64e9d38869a","abstract_canon_sha256":"afe0d6314e3658a825a4f54260fb48daa5d94ac39f52c90a37e05e82f54e5a05"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:27.673101Z","signature_b64":"hkYrrFP6D4XyU//wI+N5V+TH9ckifCmT2L/pvNI5DKgZsq3/g+Jctzaude7BNKVZ0mbwVl2w+xaBLej5GeTxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"701bee2732073c3564a74a35918bdea909f7dab7ae6667946820fb6b3d88ec70","last_reissued_at":"2026-05-17T23:39:27.671617Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:27.671617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arnau Oliver, Daniel S. Asfaw, Jose Bernal, Kaisar Kushibar, Robert Mart\\'i, Sergi Valverde, Xavier Llad\\'o","submitted_at":"2017-12-11T12:25:30Z","abstract_excerpt":"In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.03747","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":"1712.03747","created_at":"2026-05-17T23:39:27.672367+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.03747v3","created_at":"2026-05-17T23:39:27.672367+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03747","created_at":"2026-05-17T23:39:27.672367+00:00"},{"alias_kind":"pith_short_12","alias_value":"OAN64JZSA46D","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OAN64JZSA46DKZFH","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OAN64JZS","created_at":"2026-05-18T12:31:34.259226+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/OAN64JZSA46DKZFHJI2ZDC66VE","json":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE.json","graph_json":"https://pith.science/api/pith-number/OAN64JZSA46DKZFHJI2ZDC66VE/graph.json","events_json":"https://pith.science/api/pith-number/OAN64JZSA46DKZFHJI2ZDC66VE/events.json","paper":"https://pith.science/paper/OAN64JZS"},"agent_actions":{"view_html":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE","download_json":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE.json","view_paper":"https://pith.science/paper/OAN64JZS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.03747&json=true","fetch_graph":"https://pith.science/api/pith-number/OAN64JZSA46DKZFHJI2ZDC66VE/graph.json","fetch_events":"https://pith.science/api/pith-number/OAN64JZSA46DKZFHJI2ZDC66VE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE/action/storage_attestation","attest_author":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE/action/author_attestation","sign_citation":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE/action/citation_signature","submit_replication":"https://pith.science/pith/OAN64JZSA46DKZFHJI2ZDC66VE/action/replication_record"}},"created_at":"2026-05-17T23:39:27.672367+00:00","updated_at":"2026-05-17T23:39:27.672367+00:00"}