{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BI4M5YDSALK5SP4OMN422AY3NT","short_pith_number":"pith:BI4M5YDS","schema_version":"1.0","canonical_sha256":"0a38cee07202d5d93f8e6379ad031b6ce6a1b1d1af271abfcad91a14d079d050","source":{"kind":"arxiv","id":"2605.21970","version":1},"attestation_state":"computed","paper":{"title":"Entropy-Guided Self-Supervised Learning for Medical Image Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Joao Florindo, Viviane Moura","submitted_at":"2026-05-21T04:01:06Z","abstract_excerpt":"Accurate and robust medical image classification is paramount for early disease diagnosis and treatment planning. However, challenges such as limited annotated data, high intra-class variability, and subtle inter-class differences often hinder the performance of deep learning models. This paper introduces a synergistic deep learning framework that leverages the strengths of self-supervised learning and transfer learning for enhanced medical image classification. Our approach employs two distinct ConvNeXt-Tiny models: one pre-trained on a large-scale natural image dataset (ImageNet) and another"},"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":"2605.21970","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-21T04:01:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"89d2891b07ebb09ecdf7a99bf2d8b6e7d0433b72e8d69b6518e1994c6ad4ee17","abstract_canon_sha256":"065e41f4a02b0280121d300510a7229bac38e60166cd6ab6f87ca59f68e42b80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:17.865756Z","signature_b64":"WaJ24O3d/mqHeTMUP9st0OGxaVUF9lYAU5ixHtzugNyPWrJbFqIL7xS6vDb4zZ1zEvD67/d8bnIwA6V+vK5tCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a38cee07202d5d93f8e6379ad031b6ce6a1b1d1af271abfcad91a14d079d050","last_reissued_at":"2026-05-22T01:04:17.864975Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:17.864975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Entropy-Guided Self-Supervised Learning for Medical Image Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Joao Florindo, Viviane Moura","submitted_at":"2026-05-21T04:01:06Z","abstract_excerpt":"Accurate and robust medical image classification is paramount for early disease diagnosis and treatment planning. However, challenges such as limited annotated data, high intra-class variability, and subtle inter-class differences often hinder the performance of deep learning models. This paper introduces a synergistic deep learning framework that leverages the strengths of self-supervised learning and transfer learning for enhanced medical image classification. Our approach employs two distinct ConvNeXt-Tiny models: one pre-trained on a large-scale natural image dataset (ImageNet) and another"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21970","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/2605.21970/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":"2605.21970","created_at":"2026-05-22T01:04:17.865085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.21970v1","created_at":"2026-05-22T01:04:17.865085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21970","created_at":"2026-05-22T01:04:17.865085+00:00"},{"alias_kind":"pith_short_12","alias_value":"BI4M5YDSALK5","created_at":"2026-05-22T01:04:17.865085+00:00"},{"alias_kind":"pith_short_16","alias_value":"BI4M5YDSALK5SP4O","created_at":"2026-05-22T01:04:17.865085+00:00"},{"alias_kind":"pith_short_8","alias_value":"BI4M5YDS","created_at":"2026-05-22T01:04:17.865085+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/BI4M5YDSALK5SP4OMN422AY3NT","json":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT.json","graph_json":"https://pith.science/api/pith-number/BI4M5YDSALK5SP4OMN422AY3NT/graph.json","events_json":"https://pith.science/api/pith-number/BI4M5YDSALK5SP4OMN422AY3NT/events.json","paper":"https://pith.science/paper/BI4M5YDS"},"agent_actions":{"view_html":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT","download_json":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT.json","view_paper":"https://pith.science/paper/BI4M5YDS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.21970&json=true","fetch_graph":"https://pith.science/api/pith-number/BI4M5YDSALK5SP4OMN422AY3NT/graph.json","fetch_events":"https://pith.science/api/pith-number/BI4M5YDSALK5SP4OMN422AY3NT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT/action/storage_attestation","attest_author":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT/action/author_attestation","sign_citation":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT/action/citation_signature","submit_replication":"https://pith.science/pith/BI4M5YDSALK5SP4OMN422AY3NT/action/replication_record"}},"created_at":"2026-05-22T01:04:17.865085+00:00","updated_at":"2026-05-22T01:04:17.865085+00:00"}