{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3GO6G3X7OWKWSLBOQZ2XGI55NY","short_pith_number":"pith:3GO6G3X7","schema_version":"1.0","canonical_sha256":"d99de36eff7595692c2e86757323bd6e1aa7d2eaa8f8ff981c58b5a46d86db67","source":{"kind":"arxiv","id":"2209.09880","version":1},"attestation_state":"computed","paper":{"title":"Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Agapi Davradou, Anastasios Doulamis, Eftychios Protopapadakis, Maria Kaselimi, Nikolaos Doulamis","submitted_at":"2022-09-20T17:35:49Z","abstract_excerpt":"Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes. The care of DFU patients can be substantially improved through self-management, in order to achieve early-diagnosis, ulcer prevention, and complications management in existing ulcers. In this paper, we investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers: noise reduction and super-resolution. In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoenco"},"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":"2209.09880","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-09-20T17:35:49Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c76fa7385a4bc1168a73f13f2060c08d7c22213578ca88585a8b13853f89a301","abstract_canon_sha256":"edf6a95bc7bbc97c4ec35f42b5349644f0618039aa9735096dcdac58c16594b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:59:35.590405Z","signature_b64":"xzTd0JfNIDeivzFxLlqU/fT8YEFxw7CtT+TFNtF7RpaDcGLgU3rBd2OKQdM6HtLwNpylrJJg8R2IcJ00GCK3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d99de36eff7595692c2e86757323bd6e1aa7d2eaa8f8ff981c58b5a46d86db67","last_reissued_at":"2026-07-05T04:59:35.589943Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:59:35.589943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Agapi Davradou, Anastasios Doulamis, Eftychios Protopapadakis, Maria Kaselimi, Nikolaos Doulamis","submitted_at":"2022-09-20T17:35:49Z","abstract_excerpt":"Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes. The care of DFU patients can be substantially improved through self-management, in order to achieve early-diagnosis, ulcer prevention, and complications management in existing ulcers. In this paper, we investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers: noise reduction and super-resolution. In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoenco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.09880","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/2209.09880/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":"2209.09880","created_at":"2026-07-05T04:59:35.590001+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.09880v1","created_at":"2026-07-05T04:59:35.590001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.09880","created_at":"2026-07-05T04:59:35.590001+00:00"},{"alias_kind":"pith_short_12","alias_value":"3GO6G3X7OWKW","created_at":"2026-07-05T04:59:35.590001+00:00"},{"alias_kind":"pith_short_16","alias_value":"3GO6G3X7OWKWSLBO","created_at":"2026-07-05T04:59:35.590001+00:00"},{"alias_kind":"pith_short_8","alias_value":"3GO6G3X7","created_at":"2026-07-05T04:59:35.590001+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/3GO6G3X7OWKWSLBOQZ2XGI55NY","json":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY.json","graph_json":"https://pith.science/api/pith-number/3GO6G3X7OWKWSLBOQZ2XGI55NY/graph.json","events_json":"https://pith.science/api/pith-number/3GO6G3X7OWKWSLBOQZ2XGI55NY/events.json","paper":"https://pith.science/paper/3GO6G3X7"},"agent_actions":{"view_html":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY","download_json":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY.json","view_paper":"https://pith.science/paper/3GO6G3X7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.09880&json=true","fetch_graph":"https://pith.science/api/pith-number/3GO6G3X7OWKWSLBOQZ2XGI55NY/graph.json","fetch_events":"https://pith.science/api/pith-number/3GO6G3X7OWKWSLBOQZ2XGI55NY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY/action/storage_attestation","attest_author":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY/action/author_attestation","sign_citation":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY/action/citation_signature","submit_replication":"https://pith.science/pith/3GO6G3X7OWKWSLBOQZ2XGI55NY/action/replication_record"}},"created_at":"2026-07-05T04:59:35.590001+00:00","updated_at":"2026-07-05T04:59:35.590001+00:00"}