{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QDK625DQ4YT56J66XC3OWOAGXH","short_pith_number":"pith:QDK625DQ","schema_version":"1.0","canonical_sha256":"80d5ed7470e627df27deb8b6eb3806b9d2a6f27a905d431da951e48ef515530c","source":{"kind":"arxiv","id":"1808.07954","version":3},"attestation_state":"computed","paper":{"title":"From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anastasia Oikonomou, Arash Mohammadi, Habib Benali, Konstantinos N. Plataniotis, Parnian Afshar","submitted_at":"2018-08-23T21:39:12Z","abstract_excerpt":"Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in \"Radiomics\". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of "},"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":"1808.07954","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-23T21:39:12Z","cross_cats_sorted":[],"title_canon_sha256":"56c57587876a433b6240d0271452bca6be390afc2bfdc8b5bf770f9b495db2ef","abstract_canon_sha256":"f138cc9a78db54205331288e37d45677f8f9c3c344593d3f3a222140f9e81ba4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:48.597993Z","signature_b64":"G4YELyl3FPSolgNUPBozaJX+ZM7u3RCqzHXSFNIdnoBboXVf46AU9otYR6RvFzjM9px0emoE1wCYRroYYl3iAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80d5ed7470e627df27deb8b6eb3806b9d2a6f27a905d431da951e48ef515530c","last_reissued_at":"2026-05-17T23:39:48.597427Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:48.597427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anastasia Oikonomou, Arash Mohammadi, Habib Benali, Konstantinos N. Plataniotis, Parnian Afshar","submitted_at":"2018-08-23T21:39:12Z","abstract_excerpt":"Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in \"Radiomics\". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07954","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":"1808.07954","created_at":"2026-05-17T23:39:48.597519+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.07954v3","created_at":"2026-05-17T23:39:48.597519+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07954","created_at":"2026-05-17T23:39:48.597519+00:00"},{"alias_kind":"pith_short_12","alias_value":"QDK625DQ4YT5","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QDK625DQ4YT56J66","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QDK625DQ","created_at":"2026-05-18T12:32:46.962924+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/QDK625DQ4YT56J66XC3OWOAGXH","json":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH.json","graph_json":"https://pith.science/api/pith-number/QDK625DQ4YT56J66XC3OWOAGXH/graph.json","events_json":"https://pith.science/api/pith-number/QDK625DQ4YT56J66XC3OWOAGXH/events.json","paper":"https://pith.science/paper/QDK625DQ"},"agent_actions":{"view_html":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH","download_json":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH.json","view_paper":"https://pith.science/paper/QDK625DQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.07954&json=true","fetch_graph":"https://pith.science/api/pith-number/QDK625DQ4YT56J66XC3OWOAGXH/graph.json","fetch_events":"https://pith.science/api/pith-number/QDK625DQ4YT56J66XC3OWOAGXH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH/action/storage_attestation","attest_author":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH/action/author_attestation","sign_citation":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH/action/citation_signature","submit_replication":"https://pith.science/pith/QDK625DQ4YT56J66XC3OWOAGXH/action/replication_record"}},"created_at":"2026-05-17T23:39:48.597519+00:00","updated_at":"2026-05-17T23:39:48.597519+00:00"}