{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PGTC2GMME3E4624A5K5TLU7KHA","short_pith_number":"pith:PGTC2GMM","schema_version":"1.0","canonical_sha256":"79a62d198c26c9cf6b80eabb35d3ea382b6527f8cc1fc3e55b6373033a4243ce","source":{"kind":"arxiv","id":"2402.01410","version":1},"attestation_state":"computed","paper":{"title":"XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alceu Bissoto, Carlos Santiago, Catarina Barata, Miguel Correia","submitted_at":"2024-02-02T13:42:45Z","abstract_excerpt":"Skin cancer detection through dermoscopy image analysis is a critical task. However, existing models used for this purpose often lack interpretability and reliability, raising the concern of physicians due to their black-box nature. In this paper, we propose a novel approach for the diagnosis of melanoma using an interpretable prototypical-part model. We introduce a guided supervision based on non-expert feedback through the incorporation of: 1) binary masks, obtained automatically using a segmentation network; and 2) user-refined prototypes. These two distinct information pathways aim to ensu"},"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":"2402.01410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-02T13:42:45Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ccebf5cca6246c147be103b5abf109a76ec9d003dce3cf2f2d159043f4a1a8da","abstract_canon_sha256":"fa4cba117bd40b5b9171eaa7bdab9688353c6bc5b2fa2d9e1af40b33838e932e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:40:34.507711Z","signature_b64":"KCYUBX/kosahmQu57K/2G03gMV4XxBkbJjXqDahWFYYKROGdZakqsnpOyvfKtyWXBhP4hcJQLuQ6e9Xq7HY0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79a62d198c26c9cf6b80eabb35d3ea382b6527f8cc1fc3e55b6373033a4243ce","last_reissued_at":"2026-07-05T07:40:34.507288Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:40:34.507288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alceu Bissoto, Carlos Santiago, Catarina Barata, Miguel Correia","submitted_at":"2024-02-02T13:42:45Z","abstract_excerpt":"Skin cancer detection through dermoscopy image analysis is a critical task. However, existing models used for this purpose often lack interpretability and reliability, raising the concern of physicians due to their black-box nature. In this paper, we propose a novel approach for the diagnosis of melanoma using an interpretable prototypical-part model. We introduce a guided supervision based on non-expert feedback through the incorporation of: 1) binary masks, obtained automatically using a segmentation network; and 2) user-refined prototypes. These two distinct information pathways aim to ensu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01410","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/2402.01410/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":"2402.01410","created_at":"2026-07-05T07:40:34.507353+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.01410v1","created_at":"2026-07-05T07:40:34.507353+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01410","created_at":"2026-07-05T07:40:34.507353+00:00"},{"alias_kind":"pith_short_12","alias_value":"PGTC2GMME3E4","created_at":"2026-07-05T07:40:34.507353+00:00"},{"alias_kind":"pith_short_16","alias_value":"PGTC2GMME3E4624A","created_at":"2026-07-05T07:40:34.507353+00:00"},{"alias_kind":"pith_short_8","alias_value":"PGTC2GMM","created_at":"2026-07-05T07:40:34.507353+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05666","citing_title":"Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA","json":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA.json","graph_json":"https://pith.science/api/pith-number/PGTC2GMME3E4624A5K5TLU7KHA/graph.json","events_json":"https://pith.science/api/pith-number/PGTC2GMME3E4624A5K5TLU7KHA/events.json","paper":"https://pith.science/paper/PGTC2GMM"},"agent_actions":{"view_html":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA","download_json":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA.json","view_paper":"https://pith.science/paper/PGTC2GMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.01410&json=true","fetch_graph":"https://pith.science/api/pith-number/PGTC2GMME3E4624A5K5TLU7KHA/graph.json","fetch_events":"https://pith.science/api/pith-number/PGTC2GMME3E4624A5K5TLU7KHA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA/action/storage_attestation","attest_author":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA/action/author_attestation","sign_citation":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA/action/citation_signature","submit_replication":"https://pith.science/pith/PGTC2GMME3E4624A5K5TLU7KHA/action/replication_record"}},"created_at":"2026-07-05T07:40:34.507353+00:00","updated_at":"2026-07-05T07:40:34.507353+00:00"}