{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:DWS725MS6UL7LZ5Q7XRPFTQJNM","short_pith_number":"pith:DWS725MS","schema_version":"1.0","canonical_sha256":"1da5fd7592f517f5e7b0fde2f2ce096b011578d9016d9b4592e10e4cdf79820a","source":{"kind":"arxiv","id":"1703.10480","version":2},"attestation_state":"computed","paper":{"title":"A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dris Kharroubi, Jose Dolz, Laurent Massoptier, Mathilde Quidet, Maximilien Vermandel, Nacim Betrouni, Nicolas Reyns","submitted_at":"2017-03-30T14:09:53Z","abstract_excerpt":"Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported. however, marginal results when analyzing organs in the op"},"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":"1703.10480","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-30T14:09:53Z","cross_cats_sorted":[],"title_canon_sha256":"af52b77c82248ff86a48b4093c2a3f5f9059068406d2e321489d2cb644e5d445","abstract_canon_sha256":"0c8b56980422b090fb7dd4145a199f1af8c5bc694a6b82cdfb78bf796baae173"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:58.900205Z","signature_b64":"XbGq5pAFAT6U6a/Yl+GjitPYOOEwAroVYECpgaW+OcNHyfGoQJ/peEnvaxd6GR8pWSH15g5BNSBZJhfK0oVuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1da5fd7592f517f5e7b0fde2f2ce096b011578d9016d9b4592e10e4cdf79820a","last_reissued_at":"2026-05-18T00:46:58.899744Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:58.899744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dris Kharroubi, Jose Dolz, Laurent Massoptier, Mathilde Quidet, Maximilien Vermandel, Nacim Betrouni, Nicolas Reyns","submitted_at":"2017-03-30T14:09:53Z","abstract_excerpt":"Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported. however, marginal results when analyzing organs in the op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.10480","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":"1703.10480","created_at":"2026-05-18T00:46:58.899817+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.10480v2","created_at":"2026-05-18T00:46:58.899817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.10480","created_at":"2026-05-18T00:46:58.899817+00:00"},{"alias_kind":"pith_short_12","alias_value":"DWS725MS6UL7","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"DWS725MS6UL7LZ5Q","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"DWS725MS","created_at":"2026-05-18T12:31:12.930513+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/DWS725MS6UL7LZ5Q7XRPFTQJNM","json":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM.json","graph_json":"https://pith.science/api/pith-number/DWS725MS6UL7LZ5Q7XRPFTQJNM/graph.json","events_json":"https://pith.science/api/pith-number/DWS725MS6UL7LZ5Q7XRPFTQJNM/events.json","paper":"https://pith.science/paper/DWS725MS"},"agent_actions":{"view_html":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM","download_json":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM.json","view_paper":"https://pith.science/paper/DWS725MS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.10480&json=true","fetch_graph":"https://pith.science/api/pith-number/DWS725MS6UL7LZ5Q7XRPFTQJNM/graph.json","fetch_events":"https://pith.science/api/pith-number/DWS725MS6UL7LZ5Q7XRPFTQJNM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM/action/storage_attestation","attest_author":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM/action/author_attestation","sign_citation":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM/action/citation_signature","submit_replication":"https://pith.science/pith/DWS725MS6UL7LZ5Q7XRPFTQJNM/action/replication_record"}},"created_at":"2026-05-18T00:46:58.899817+00:00","updated_at":"2026-05-18T00:46:58.899817+00:00"}