{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LEWMLZL54MNT4K4WJC72S4GB4I","short_pith_number":"pith:LEWMLZL5","schema_version":"1.0","canonical_sha256":"592cc5e57de31b3e2b9648bfa970c1e2354d6c3da76dc4ed485201075a000e18","source":{"kind":"arxiv","id":"2405.02334","version":2},"attestation_state":"computed","paper":{"title":"Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Calogero Zarcaro, Carmelo Militello, Francesco Prinzi, Salvatore Gaglio, Salvatore Vitabile, Tommaso Vincenzo Bartolotta","submitted_at":"2024-04-26T15:02:39Z","abstract_excerpt":"In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power"},"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":"2405.02334","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-26T15:02:39Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3e51ae3e6f397da74e219ad81152ba2fdc3fd01c3dffcc4fd740f255938a97a5","abstract_canon_sha256":"2b7a24e5763790c1cf56d316183f395c15248f685308064a3a7647b81019219e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:20.229826Z","signature_b64":"jWkM0xMjZO6wQWtFhUD9/vB/ZUnL9NRHtASg4tDGfSXNfqATtPhC8F7Iot+fXq5Ej6iHviRx18Yhh9MrV7oXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"592cc5e57de31b3e2b9648bfa970c1e2354d6c3da76dc4ed485201075a000e18","last_reissued_at":"2026-07-05T09:58:20.229292Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:20.229292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Calogero Zarcaro, Carmelo Militello, Francesco Prinzi, Salvatore Gaglio, Salvatore Vitabile, Tommaso Vincenzo Bartolotta","submitted_at":"2024-04-26T15:02:39Z","abstract_excerpt":"In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02334","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.02334/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2405.02334","created_at":"2026-07-05T09:58:20.229358+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.02334v2","created_at":"2026-07-05T09:58:20.229358+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02334","created_at":"2026-07-05T09:58:20.229358+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEWMLZL54MNT","created_at":"2026-07-05T09:58:20.229358+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEWMLZL54MNT4K4W","created_at":"2026-07-05T09:58:20.229358+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEWMLZL5","created_at":"2026-07-05T09:58:20.229358+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/LEWMLZL54MNT4K4WJC72S4GB4I","json":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I.json","graph_json":"https://pith.science/api/pith-number/LEWMLZL54MNT4K4WJC72S4GB4I/graph.json","events_json":"https://pith.science/api/pith-number/LEWMLZL54MNT4K4WJC72S4GB4I/events.json","paper":"https://pith.science/paper/LEWMLZL5"},"agent_actions":{"view_html":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I","download_json":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I.json","view_paper":"https://pith.science/paper/LEWMLZL5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.02334&json=true","fetch_graph":"https://pith.science/api/pith-number/LEWMLZL54MNT4K4WJC72S4GB4I/graph.json","fetch_events":"https://pith.science/api/pith-number/LEWMLZL54MNT4K4WJC72S4GB4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I/action/storage_attestation","attest_author":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I/action/author_attestation","sign_citation":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I/action/citation_signature","submit_replication":"https://pith.science/pith/LEWMLZL54MNT4K4WJC72S4GB4I/action/replication_record"}},"created_at":"2026-07-05T09:58:20.229358+00:00","updated_at":"2026-07-05T09:58:20.229358+00:00"}