{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:VD47EJA7AYKSRRRBW7HW45Q6HV","short_pith_number":"pith:VD47EJA7","schema_version":"1.0","canonical_sha256":"a8f9f2241f061528c621b7cf6e761e3d6590d1e10f3a1e9c90298e989ba22804","source":{"kind":"arxiv","id":"1710.08354","version":1},"attestation_state":"computed","paper":{"title":"Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Let\\'icia Rittner, Oeslle Lucena, Richard Frayne, Roberto Lotufo, Roberto Souza","submitted_at":"2017-10-23T15:57:45Z","abstract_excerpt":"The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the \"gold-standard\". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimat"},"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":"1710.08354","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2017-10-23T15:57:45Z","cross_cats_sorted":[],"title_canon_sha256":"1d739aeddf295b196667a8ca644c54828fe30e53e34fdcd978d6c1f8ffd92424","abstract_canon_sha256":"d58da5b2ea69f668e61bcb315f55f299f03030e77d385ccfd7a1761ded00aa12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:16.618123Z","signature_b64":"XzIat8HHvPfbGXKr1k7MYNqwm4YftvN3zvf0nJ9Xq+GW/75MeQMmoZ2UQUQ3iYqSCvWKjkrTly1PbW4PIw37DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8f9f2241f061528c621b7cf6e761e3d6590d1e10f3a1e9c90298e989ba22804","last_reissued_at":"2026-05-18T00:32:16.617638Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:16.617638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Let\\'icia Rittner, Oeslle Lucena, Richard Frayne, Roberto Lotufo, Roberto Souza","submitted_at":"2017-10-23T15:57:45Z","abstract_excerpt":"The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the \"gold-standard\". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08354","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":""},"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":"1710.08354","created_at":"2026-05-18T00:32:16.617711+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.08354v1","created_at":"2026-05-18T00:32:16.617711+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08354","created_at":"2026-05-18T00:32:16.617711+00:00"},{"alias_kind":"pith_short_12","alias_value":"VD47EJA7AYKS","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"VD47EJA7AYKSRRRB","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"VD47EJA7","created_at":"2026-05-18T12:31:49.984773+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/VD47EJA7AYKSRRRBW7HW45Q6HV","json":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV.json","graph_json":"https://pith.science/api/pith-number/VD47EJA7AYKSRRRBW7HW45Q6HV/graph.json","events_json":"https://pith.science/api/pith-number/VD47EJA7AYKSRRRBW7HW45Q6HV/events.json","paper":"https://pith.science/paper/VD47EJA7"},"agent_actions":{"view_html":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV","download_json":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV.json","view_paper":"https://pith.science/paper/VD47EJA7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.08354&json=true","fetch_graph":"https://pith.science/api/pith-number/VD47EJA7AYKSRRRBW7HW45Q6HV/graph.json","fetch_events":"https://pith.science/api/pith-number/VD47EJA7AYKSRRRBW7HW45Q6HV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV/action/storage_attestation","attest_author":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV/action/author_attestation","sign_citation":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV/action/citation_signature","submit_replication":"https://pith.science/pith/VD47EJA7AYKSRRRBW7HW45Q6HV/action/replication_record"}},"created_at":"2026-05-18T00:32:16.617711+00:00","updated_at":"2026-05-18T00:32:16.617711+00:00"}