{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WC2KLAAAE7EWVQZ65XRC3RGRYQ","short_pith_number":"pith:WC2KLAAA","schema_version":"1.0","canonical_sha256":"b0b4a5800027c96ac33eede22dc4d1c43832b3c538b3b80ecb69fef441e83497","source":{"kind":"arxiv","id":"1909.04525","version":2},"attestation_state":"computed","paper":{"title":"Skin cancer detection based on deep learning and entropy to detect outlier samples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abder-Rahman Ali, Andre G. C. Pacheco, Thomas Trappenberg","submitted_at":"2019-09-10T14:36:16Z","abstract_excerpt":"We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we de"},"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":"1909.04525","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-09-10T14:36:16Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"507a535ea584822ca95e69788cac1b9a041535c81363afd8cfede137ac2772af","abstract_canon_sha256":"73fd0a3bd74983fb8df6da9a4943c1da64320e29af8137e598a015f8e32de821"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:29:39.970158Z","signature_b64":"aYC+sFPs2hRiAUUS4dHED3TlL6ZE1RJrUMOEr4WFne4kG6lK3BN3Qs3YrC7pX0HC7zUs1QZ0TBPSy/j+2DycBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0b4a5800027c96ac33eede22dc4d1c43832b3c538b3b80ecb69fef441e83497","last_reissued_at":"2026-07-05T00:29:39.969782Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:29:39.969782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Skin cancer detection based on deep learning and entropy to detect outlier samples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abder-Rahman Ali, Andre G. C. Pacheco, Thomas Trappenberg","submitted_at":"2019-09-10T14:36:16Z","abstract_excerpt":"We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.04525","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/1909.04525/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":"1909.04525","created_at":"2026-07-05T00:29:39.969838+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.04525v2","created_at":"2026-07-05T00:29:39.969838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.04525","created_at":"2026-07-05T00:29:39.969838+00:00"},{"alias_kind":"pith_short_12","alias_value":"WC2KLAAAE7EW","created_at":"2026-07-05T00:29:39.969838+00:00"},{"alias_kind":"pith_short_16","alias_value":"WC2KLAAAE7EWVQZ6","created_at":"2026-07-05T00:29:39.969838+00:00"},{"alias_kind":"pith_short_8","alias_value":"WC2KLAAA","created_at":"2026-07-05T00:29:39.969838+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/WC2KLAAAE7EWVQZ65XRC3RGRYQ","json":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ.json","graph_json":"https://pith.science/api/pith-number/WC2KLAAAE7EWVQZ65XRC3RGRYQ/graph.json","events_json":"https://pith.science/api/pith-number/WC2KLAAAE7EWVQZ65XRC3RGRYQ/events.json","paper":"https://pith.science/paper/WC2KLAAA"},"agent_actions":{"view_html":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ","download_json":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ.json","view_paper":"https://pith.science/paper/WC2KLAAA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.04525&json=true","fetch_graph":"https://pith.science/api/pith-number/WC2KLAAAE7EWVQZ65XRC3RGRYQ/graph.json","fetch_events":"https://pith.science/api/pith-number/WC2KLAAAE7EWVQZ65XRC3RGRYQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ/action/storage_attestation","attest_author":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ/action/author_attestation","sign_citation":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ/action/citation_signature","submit_replication":"https://pith.science/pith/WC2KLAAAE7EWVQZ65XRC3RGRYQ/action/replication_record"}},"created_at":"2026-07-05T00:29:39.969838+00:00","updated_at":"2026-07-05T00:29:39.969838+00:00"}