{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NCNWDXTZAVJIP5MMB6I53U32OM","short_pith_number":"pith:NCNWDXTZ","schema_version":"1.0","canonical_sha256":"689b61de79055287f58c0f91ddd37a73350c0407b972d66b17654c784d50827b","source":{"kind":"arxiv","id":"1710.05006","version":3},"attestation_state":"computed","paper":{"title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aadi Kalloo, Allan Halpern, Brian Helba, David Gutman, Harald Kittler, Konstantinos Liopyris, M. Emre Celebi, Michael A. Marchetti, Nabin Mishra, Noel C. F. Codella, Stephen W. Dusza","submitted_at":"2017-10-13T17:08:53Z","abstract_excerpt":"This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this f"},"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":"1710.05006","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-13T17:08:53Z","cross_cats_sorted":[],"title_canon_sha256":"022190c25e2fd6e3dd609ea1c7fd508ae4c300154ef95e74d4a60526a2288c71","abstract_canon_sha256":"8dad603d1731a99b105b0a770d438ba38c7270aeb198279869caac2d926da38c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:36.280717Z","signature_b64":"M1HG9aLW+PDr6vIcr7EfEXs2E256JprEfpgF6dPh4hu+yzXRdxAMZ1mNxaRoN4PUEqAkLKoVAxoZxXIsV3hgBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"689b61de79055287f58c0f91ddd37a73350c0407b972d66b17654c784d50827b","last_reissued_at":"2026-05-18T00:26:36.280163Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:36.280163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aadi Kalloo, Allan Halpern, Brian Helba, David Gutman, Harald Kittler, Konstantinos Liopyris, M. Emre Celebi, Michael A. Marchetti, Nabin Mishra, Noel C. F. Codella, Stephen W. Dusza","submitted_at":"2017-10-13T17:08:53Z","abstract_excerpt":"This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.05006","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":"1710.05006","created_at":"2026-05-18T00:26:36.280258+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.05006v3","created_at":"2026-05-18T00:26:36.280258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.05006","created_at":"2026-05-18T00:26:36.280258+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCNWDXTZAVJI","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCNWDXTZAVJIP5MM","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCNWDXTZ","created_at":"2026-05-18T12:31:31.346846+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.02435","citing_title":"MARVEL: Margin-Aware Robust von Mises-Fischer Expert Learning for Long-Tailed Out-of-Distribution Detection","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10261","citing_title":"E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01667","citing_title":"Deep neural networks with Fisher vector encoding for medical image classification","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05081","citing_title":"MedGemma 1.5 Technical Report","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM","json":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM.json","graph_json":"https://pith.science/api/pith-number/NCNWDXTZAVJIP5MMB6I53U32OM/graph.json","events_json":"https://pith.science/api/pith-number/NCNWDXTZAVJIP5MMB6I53U32OM/events.json","paper":"https://pith.science/paper/NCNWDXTZ"},"agent_actions":{"view_html":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM","download_json":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM.json","view_paper":"https://pith.science/paper/NCNWDXTZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.05006&json=true","fetch_graph":"https://pith.science/api/pith-number/NCNWDXTZAVJIP5MMB6I53U32OM/graph.json","fetch_events":"https://pith.science/api/pith-number/NCNWDXTZAVJIP5MMB6I53U32OM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM/action/storage_attestation","attest_author":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM/action/author_attestation","sign_citation":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM/action/citation_signature","submit_replication":"https://pith.science/pith/NCNWDXTZAVJIP5MMB6I53U32OM/action/replication_record"}},"created_at":"2026-05-18T00:26:36.280258+00:00","updated_at":"2026-05-18T00:26:36.280258+00:00"}