{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RB6OQ6RR5OS7PHWQFBGBUE7IL5","short_pith_number":"pith:RB6OQ6RR","schema_version":"1.0","canonical_sha256":"887ce87a31eba5f79ed0284c1a13e85f50bc75a485777dd2598cf3e61b23f64f","source":{"kind":"arxiv","id":"1807.08993","version":1},"attestation_state":"computed","paper":{"title":"Deep-CLASS at ISIC Machine Learning Challenge 2018","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Julien Helsper, Madjid Fathi, Matthias Jung, Sara Nasiri","submitted_at":"2018-07-24T09:43:53Z","abstract_excerpt":"This paper reports the method and evaluation results of MedAusbild team for ISIC challenge task. Since early 2017, our team has worked on melanoma classification [1][6], and has employed deep learning since beginning of 2018 [7]. Deep learning helps researchers absolutely to treat and detect diseases by analyzing medical data (e.g., medical images). One of the representative models among the various deep-learning models is a convolutional neural network (CNN). Although our team has an experience with segmentation and classification of benign and malignant skin-lesions, we have participated in "},"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":"1807.08993","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-24T09:43:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d58b015f01f8fbbcb1d177da773086fb265e4b28c20ec218d31459a8f9a9b921","abstract_canon_sha256":"7be77f6b3ffeb21a3b0a8cdf971747be1eb140106aff4e635b20606a9481683d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:57.112507Z","signature_b64":"gcknQ4yuR6b+Gz5Nx2xqAz9k4Zzrz+8tMY/0vX8iLzIvwryvRxai7qmaBbb0fA/XUswSsYk7UtgM7HYAwOSWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"887ce87a31eba5f79ed0284c1a13e85f50bc75a485777dd2598cf3e61b23f64f","last_reissued_at":"2026-05-18T00:09:57.111846Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:57.111846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-CLASS at ISIC Machine Learning Challenge 2018","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Julien Helsper, Madjid Fathi, Matthias Jung, Sara Nasiri","submitted_at":"2018-07-24T09:43:53Z","abstract_excerpt":"This paper reports the method and evaluation results of MedAusbild team for ISIC challenge task. Since early 2017, our team has worked on melanoma classification [1][6], and has employed deep learning since beginning of 2018 [7]. Deep learning helps researchers absolutely to treat and detect diseases by analyzing medical data (e.g., medical images). One of the representative models among the various deep-learning models is a convolutional neural network (CNN). Although our team has an experience with segmentation and classification of benign and malignant skin-lesions, we have participated in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.08993","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":""},"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":"1807.08993","created_at":"2026-05-18T00:09:57.111942+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.08993v1","created_at":"2026-05-18T00:09:57.111942+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.08993","created_at":"2026-05-18T00:09:57.111942+00:00"},{"alias_kind":"pith_short_12","alias_value":"RB6OQ6RR5OS7","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RB6OQ6RR5OS7PHWQ","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RB6OQ6RR","created_at":"2026-05-18T12:32:50.500415+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/RB6OQ6RR5OS7PHWQFBGBUE7IL5","json":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5.json","graph_json":"https://pith.science/api/pith-number/RB6OQ6RR5OS7PHWQFBGBUE7IL5/graph.json","events_json":"https://pith.science/api/pith-number/RB6OQ6RR5OS7PHWQFBGBUE7IL5/events.json","paper":"https://pith.science/paper/RB6OQ6RR"},"agent_actions":{"view_html":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5","download_json":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5.json","view_paper":"https://pith.science/paper/RB6OQ6RR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.08993&json=true","fetch_graph":"https://pith.science/api/pith-number/RB6OQ6RR5OS7PHWQFBGBUE7IL5/graph.json","fetch_events":"https://pith.science/api/pith-number/RB6OQ6RR5OS7PHWQFBGBUE7IL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5/action/storage_attestation","attest_author":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5/action/author_attestation","sign_citation":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5/action/citation_signature","submit_replication":"https://pith.science/pith/RB6OQ6RR5OS7PHWQFBGBUE7IL5/action/replication_record"}},"created_at":"2026-05-18T00:09:57.111942+00:00","updated_at":"2026-05-18T00:09:57.111942+00:00"}