{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D5XZBSHPDQACU3YFGKABSIAI2V","short_pith_number":"pith:D5XZBSHP","schema_version":"1.0","canonical_sha256":"1f6f90c8ef1c002a6f053280192008d546ddaac5f887f9f9368bdd8436dd08da","source":{"kind":"arxiv","id":"2501.06887","version":1},"attestation_state":"computed","paper":{"title":"MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"cs.CV","authors_text":"Sadia Kamal, Tim Oates","submitted_at":"2025-01-12T17:50:47Z","abstract_excerpt":"As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis, while advancements in lesion detection and classification have improved accuracy, the black-box nature of these methods poses challenges in understanding their decision processes, leading to trust issues among physicians. This study leverages the CLIP (Contrastive Language-Image Pretraining) model, trained on different skin lesion datasets, to capture meaningful relationships between visual features and diagnostic criteria terms. To further enhan"},"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":"2501.06887","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-12T17:50:47Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG"],"title_canon_sha256":"f24b8908834407a5e24fb1f0c3caa526081a50906c429fc17107a567dad4611a","abstract_canon_sha256":"7f3c77551ee3b88f6e5d7ad3ae55bea8f3bc68abc914aa86f33d594aa37a3642"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:11.870616Z","signature_b64":"H+brx5TNiDjwrpOYiz01O6+ixOu/MwaLGaiG9SJCLE2jjBczRbEM2GpPhq4v30sz+uLUVGriGJnGj73C935QDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f6f90c8ef1c002a6f053280192008d546ddaac5f887f9f9368bdd8436dd08da","last_reissued_at":"2026-07-05T10:00:11.870033Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:11.870033Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"cs.CV","authors_text":"Sadia Kamal, Tim Oates","submitted_at":"2025-01-12T17:50:47Z","abstract_excerpt":"As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis, while advancements in lesion detection and classification have improved accuracy, the black-box nature of these methods poses challenges in understanding their decision processes, leading to trust issues among physicians. This study leverages the CLIP (Contrastive Language-Image Pretraining) model, trained on different skin lesion datasets, to capture meaningful relationships between visual features and diagnostic criteria terms. To further enhan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06887","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/2501.06887/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":"2501.06887","created_at":"2026-07-05T10:00:11.870092+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.06887v1","created_at":"2026-07-05T10:00:11.870092+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06887","created_at":"2026-07-05T10:00:11.870092+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5XZBSHPDQAC","created_at":"2026-07-05T10:00:11.870092+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5XZBSHPDQACU3YF","created_at":"2026-07-05T10:00:11.870092+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5XZBSHP","created_at":"2026-07-05T10:00:11.870092+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/D5XZBSHPDQACU3YFGKABSIAI2V","json":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V.json","graph_json":"https://pith.science/api/pith-number/D5XZBSHPDQACU3YFGKABSIAI2V/graph.json","events_json":"https://pith.science/api/pith-number/D5XZBSHPDQACU3YFGKABSIAI2V/events.json","paper":"https://pith.science/paper/D5XZBSHP"},"agent_actions":{"view_html":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V","download_json":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V.json","view_paper":"https://pith.science/paper/D5XZBSHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.06887&json=true","fetch_graph":"https://pith.science/api/pith-number/D5XZBSHPDQACU3YFGKABSIAI2V/graph.json","fetch_events":"https://pith.science/api/pith-number/D5XZBSHPDQACU3YFGKABSIAI2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V/action/storage_attestation","attest_author":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V/action/author_attestation","sign_citation":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V/action/citation_signature","submit_replication":"https://pith.science/pith/D5XZBSHPDQACU3YFGKABSIAI2V/action/replication_record"}},"created_at":"2026-07-05T10:00:11.870092+00:00","updated_at":"2026-07-05T10:00:11.870092+00:00"}