{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JODX3CIO4ZN272WV6TQRRF3DTM","short_pith_number":"pith:JODX3CIO","schema_version":"1.0","canonical_sha256":"4b877d890ee65bafead5f4e11897639b0f30fd00b4bd0a58cc8014a684e89fdc","source":{"kind":"arxiv","id":"2511.10367","version":2},"attestation_state":"computed","paper":{"title":"DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Emanoel Thyago, \\'Erico Medeiros, F\\'abio Papais, Francisco Mauro, J\\'essica Guido, Kelvin Cunha, Nat\\'alia Lopes, Paulo Borba, Rodrigo Abreu, Shirley Cruz, Thales Bezerra, Tsang Ing Ren","submitted_at":"2025-11-13T14:48:07Z","abstract_excerpt":"AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality checks, and local model adaptation. The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices. In preliminary experiments, models trained on public datasets failed to generalize to our samples, whil"},"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":"2511.10367","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-13T14:48:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1bb58f7f493d73984f9424c586dcb145337d6b7933fb757b8c563b6e4df7f586","abstract_canon_sha256":"6d5f5481b19f66de0fb217b9875e0be28bb75ab8d27c22c7e499d408e1927b63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:04:35.634889Z","signature_b64":"vWelXcj49kHfjTmZ4TweK79qwZosgAWFwOpeFFqv3aU/pvanKvCRIBmLdQ4TbagLj4fbPmH/3VIHWI90A12gCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b877d890ee65bafead5f4e11897639b0f30fd00b4bd0a58cc8014a684e89fdc","last_reissued_at":"2026-06-02T03:04:35.634405Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:04:35.634405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Emanoel Thyago, \\'Erico Medeiros, F\\'abio Papais, Francisco Mauro, J\\'essica Guido, Kelvin Cunha, Nat\\'alia Lopes, Paulo Borba, Rodrigo Abreu, Shirley Cruz, Thales Bezerra, Tsang Ing Ren","submitted_at":"2025-11-13T14:48:07Z","abstract_excerpt":"AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality checks, and local model adaptation. The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices. In preliminary experiments, models trained on public datasets failed to generalize to our samples, whil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.10367","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/2511.10367/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":"2511.10367","created_at":"2026-06-02T03:04:35.634470+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.10367v2","created_at":"2026-06-02T03:04:35.634470+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.10367","created_at":"2026-06-02T03:04:35.634470+00:00"},{"alias_kind":"pith_short_12","alias_value":"JODX3CIO4ZN2","created_at":"2026-06-02T03:04:35.634470+00:00"},{"alias_kind":"pith_short_16","alias_value":"JODX3CIO4ZN272WV","created_at":"2026-06-02T03:04:35.634470+00:00"},{"alias_kind":"pith_short_8","alias_value":"JODX3CIO","created_at":"2026-06-02T03:04:35.634470+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/JODX3CIO4ZN272WV6TQRRF3DTM","json":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM.json","graph_json":"https://pith.science/api/pith-number/JODX3CIO4ZN272WV6TQRRF3DTM/graph.json","events_json":"https://pith.science/api/pith-number/JODX3CIO4ZN272WV6TQRRF3DTM/events.json","paper":"https://pith.science/paper/JODX3CIO"},"agent_actions":{"view_html":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM","download_json":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM.json","view_paper":"https://pith.science/paper/JODX3CIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.10367&json=true","fetch_graph":"https://pith.science/api/pith-number/JODX3CIO4ZN272WV6TQRRF3DTM/graph.json","fetch_events":"https://pith.science/api/pith-number/JODX3CIO4ZN272WV6TQRRF3DTM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM/action/storage_attestation","attest_author":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM/action/author_attestation","sign_citation":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM/action/citation_signature","submit_replication":"https://pith.science/pith/JODX3CIO4ZN272WV6TQRRF3DTM/action/replication_record"}},"created_at":"2026-06-02T03:04:35.634470+00:00","updated_at":"2026-06-02T03:04:35.634470+00:00"}