{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AU5ICRESV23X7XFE6HKZWFKUWP","short_pith_number":"pith:AU5ICRES","schema_version":"1.0","canonical_sha256":"053a814492aeb77fdca4f1d59b1554b3c31cb8614a1cf078133c03cac81001df","source":{"kind":"arxiv","id":"2302.12084","version":1},"attestation_state":"computed","paper":{"title":"Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alfiia Galimzianova, Ole Winther, Raluca Jalaboi","submitted_at":"2023-02-23T15:16:40Z","abstract_excerpt":"In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To date, diagnosing diseases from images is one of the most explored applications of DL within dermatology. Convolutional neural networks (ConvNets) are the most common (DL) method in medical imaging due to their training efficiency and accuracy, although they are often described as black boxes because of their limited explainability. One popular way to obtain insight into a ConvNet's decision mechanism is gradient class "},"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":"2302.12084","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-23T15:16:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4e0c75db7fcf3476c1c8dc02596acf996bf851d3a01f5d3a71b8a67a4090d924","abstract_canon_sha256":"af8ea8d618804f097c39a3b2f3ac48218a875400e6199767149ef145f5ddfe5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:59.943021Z","signature_b64":"IfRB+8TNZ8r0kXpkD0NVcRqmyYRnYpH98sPkoV3QsFKEBc6BZFRpUrk4kC8B9Sb9g486gYmlh52RlrkhQkr4Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"053a814492aeb77fdca4f1d59b1554b3c31cb8614a1cf078133c03cac81001df","last_reissued_at":"2026-07-05T05:44:59.942657Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:59.942657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alfiia Galimzianova, Ole Winther, Raluca Jalaboi","submitted_at":"2023-02-23T15:16:40Z","abstract_excerpt":"In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To date, diagnosing diseases from images is one of the most explored applications of DL within dermatology. Convolutional neural networks (ConvNets) are the most common (DL) method in medical imaging due to their training efficiency and accuracy, although they are often described as black boxes because of their limited explainability. One popular way to obtain insight into a ConvNet's decision mechanism is gradient class "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.12084","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.12084/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":"2302.12084","created_at":"2026-07-05T05:44:59.942716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.12084v1","created_at":"2026-07-05T05:44:59.942716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.12084","created_at":"2026-07-05T05:44:59.942716+00:00"},{"alias_kind":"pith_short_12","alias_value":"AU5ICRESV23X","created_at":"2026-07-05T05:44:59.942716+00:00"},{"alias_kind":"pith_short_16","alias_value":"AU5ICRESV23X7XFE","created_at":"2026-07-05T05:44:59.942716+00:00"},{"alias_kind":"pith_short_8","alias_value":"AU5ICRES","created_at":"2026-07-05T05:44:59.942716+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/AU5ICRESV23X7XFE6HKZWFKUWP","json":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP.json","graph_json":"https://pith.science/api/pith-number/AU5ICRESV23X7XFE6HKZWFKUWP/graph.json","events_json":"https://pith.science/api/pith-number/AU5ICRESV23X7XFE6HKZWFKUWP/events.json","paper":"https://pith.science/paper/AU5ICRES"},"agent_actions":{"view_html":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP","download_json":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP.json","view_paper":"https://pith.science/paper/AU5ICRES","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.12084&json=true","fetch_graph":"https://pith.science/api/pith-number/AU5ICRESV23X7XFE6HKZWFKUWP/graph.json","fetch_events":"https://pith.science/api/pith-number/AU5ICRESV23X7XFE6HKZWFKUWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP/action/storage_attestation","attest_author":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP/action/author_attestation","sign_citation":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP/action/citation_signature","submit_replication":"https://pith.science/pith/AU5ICRESV23X7XFE6HKZWFKUWP/action/replication_record"}},"created_at":"2026-07-05T05:44:59.942716+00:00","updated_at":"2026-07-05T05:44:59.942716+00:00"}