{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RZL7HOVAXN5JTNS2ZIWNB7LGAW","short_pith_number":"pith:RZL7HOVA","schema_version":"1.0","canonical_sha256":"8e57f3baa0bb7a99b65aca2cd0fd66059d721b9e6f91157037e802a7c6b152da","source":{"kind":"arxiv","id":"2502.05517","version":2},"attestation_state":"computed","paper":{"title":"Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Javier S\\'anchez, \\'Oscar A. Mart\\'in","submitted_at":"2025-02-08T10:35:51Z","abstract_excerpt":"Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformers architectures, including Swin Transformer and MaxViT, in several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset includes different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims "},"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":"2502.05517","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-08T10:35:51Z","cross_cats_sorted":[],"title_canon_sha256":"69cbf6010e5fd7144b98debcdb567cb54becf9118d967d5029dc314108d3065a","abstract_canon_sha256":"393a9b6ecbe078984ea50f27abface8dcc2878b3a2249c52a2805e7144d336cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:17.665838Z","signature_b64":"sB5V8YGOO5gaPr2u7agGxZBTxCkRFcJ6EbvPwSL445WUi5wJSvEyfnMzQzgpCZSpv/jm3kmyjCkDSu8tfqRACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e57f3baa0bb7a99b65aca2cd0fd66059d721b9e6f91157037e802a7c6b152da","last_reissued_at":"2026-07-05T11:22:17.665349Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:17.665349Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Javier S\\'anchez, \\'Oscar A. Mart\\'in","submitted_at":"2025-02-08T10:35:51Z","abstract_excerpt":"Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformers architectures, including Swin Transformer and MaxViT, in several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset includes different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05517","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/2502.05517/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":"2502.05517","created_at":"2026-07-05T11:22:17.665411+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.05517v2","created_at":"2026-07-05T11:22:17.665411+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05517","created_at":"2026-07-05T11:22:17.665411+00:00"},{"alias_kind":"pith_short_12","alias_value":"RZL7HOVAXN5J","created_at":"2026-07-05T11:22:17.665411+00:00"},{"alias_kind":"pith_short_16","alias_value":"RZL7HOVAXN5JTNS2","created_at":"2026-07-05T11:22:17.665411+00:00"},{"alias_kind":"pith_short_8","alias_value":"RZL7HOVA","created_at":"2026-07-05T11:22:17.665411+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.06982","citing_title":"Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW","json":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW.json","graph_json":"https://pith.science/api/pith-number/RZL7HOVAXN5JTNS2ZIWNB7LGAW/graph.json","events_json":"https://pith.science/api/pith-number/RZL7HOVAXN5JTNS2ZIWNB7LGAW/events.json","paper":"https://pith.science/paper/RZL7HOVA"},"agent_actions":{"view_html":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW","download_json":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW.json","view_paper":"https://pith.science/paper/RZL7HOVA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.05517&json=true","fetch_graph":"https://pith.science/api/pith-number/RZL7HOVAXN5JTNS2ZIWNB7LGAW/graph.json","fetch_events":"https://pith.science/api/pith-number/RZL7HOVAXN5JTNS2ZIWNB7LGAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW/action/storage_attestation","attest_author":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW/action/author_attestation","sign_citation":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW/action/citation_signature","submit_replication":"https://pith.science/pith/RZL7HOVAXN5JTNS2ZIWNB7LGAW/action/replication_record"}},"created_at":"2026-07-05T11:22:17.665411+00:00","updated_at":"2026-07-05T11:22:17.665411+00:00"}