{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AVLLH6NTH74HZOESI2UPUBIHSJ","short_pith_number":"pith:AVLLH6NT","schema_version":"1.0","canonical_sha256":"0556b3f9b33ff87cb89246a8fa05079259f2135550a540c85efa85a5d37e069d","source":{"kind":"arxiv","id":"2301.03505","version":3},"attestation_state":"computed","paper":{"title":"Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abin Jose, Amirali Molaei, Amirhossein Kazerouni, Dorit Merhof, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Rijo Roy, Yiwei Jia","submitted_at":"2023-01-09T16:56:23Z","abstract_excerpt":"The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imag"},"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":"2301.03505","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-01-09T16:56:23Z","cross_cats_sorted":[],"title_canon_sha256":"05a7a5e5744650162cfd6a9447b87c28c3935daab08c897fbd1d203fe7d349dd","abstract_canon_sha256":"c7fd565f1e5f950c03e49108481595d55a42005ca8cc50df5cdd420cd16f39f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:54.455625Z","signature_b64":"+DXYRbOJ4beFy4cbRhsM9YQ/kiTk39Nn0NXHP0idHXKfH5nZpulZ2SkrdLPYQKEllh3qRg2p9OJA1lsJ+wknBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0556b3f9b33ff87cb89246a8fa05079259f2135550a540c85efa85a5d37e069d","last_reissued_at":"2026-07-05T07:08:54.455176Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:54.455176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abin Jose, Amirali Molaei, Amirhossein Kazerouni, Dorit Merhof, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Rijo Roy, Yiwei Jia","submitted_at":"2023-01-09T16:56:23Z","abstract_excerpt":"The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.03505","kind":"arxiv","version":3},"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/2301.03505/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":"2301.03505","created_at":"2026-07-05T07:08:54.455231+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.03505v3","created_at":"2026-07-05T07:08:54.455231+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03505","created_at":"2026-07-05T07:08:54.455231+00:00"},{"alias_kind":"pith_short_12","alias_value":"AVLLH6NTH74H","created_at":"2026-07-05T07:08:54.455231+00:00"},{"alias_kind":"pith_short_16","alias_value":"AVLLH6NTH74HZOES","created_at":"2026-07-05T07:08:54.455231+00:00"},{"alias_kind":"pith_short_8","alias_value":"AVLLH6NT","created_at":"2026-07-05T07:08:54.455231+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/AVLLH6NTH74HZOESI2UPUBIHSJ","json":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ.json","graph_json":"https://pith.science/api/pith-number/AVLLH6NTH74HZOESI2UPUBIHSJ/graph.json","events_json":"https://pith.science/api/pith-number/AVLLH6NTH74HZOESI2UPUBIHSJ/events.json","paper":"https://pith.science/paper/AVLLH6NT"},"agent_actions":{"view_html":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ","download_json":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ.json","view_paper":"https://pith.science/paper/AVLLH6NT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.03505&json=true","fetch_graph":"https://pith.science/api/pith-number/AVLLH6NTH74HZOESI2UPUBIHSJ/graph.json","fetch_events":"https://pith.science/api/pith-number/AVLLH6NTH74HZOESI2UPUBIHSJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ/action/storage_attestation","attest_author":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ/action/author_attestation","sign_citation":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ/action/citation_signature","submit_replication":"https://pith.science/pith/AVLLH6NTH74HZOESI2UPUBIHSJ/action/replication_record"}},"created_at":"2026-07-05T07:08:54.455231+00:00","updated_at":"2026-07-05T07:08:54.455231+00:00"}