{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CVMR6XTUO67H5GDWR7SX5I5SSO","short_pith_number":"pith:CVMR6XTU","schema_version":"1.0","canonical_sha256":"15591f5e7477be7e98768fe57ea3b293abfadd63d392c266245e0c079cb7c00d","source":{"kind":"arxiv","id":"2105.06986","version":1},"attestation_state":"computed","paper":{"title":"Evaluating the Robustness of Self-Supervised Learning in Medical Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anjany Sekuboyina, Bjoern H. Menze, Christopher Watanabe, Fernando Navarro, Jan C. Peeken, Stephanie E. Combs, Suprosanna Shit","submitted_at":"2021-05-14T17:49:52Z","abstract_excerpt":"Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to fully-supervised learning. Meanwhile, little attention has been given to study the robustness of networks trained in a self-supervised manner. In this work, we demonstrate that networks trained via self-supervised learning have superior robustness and generalizability compared t"},"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":"2105.06986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-05-14T17:49:52Z","cross_cats_sorted":[],"title_canon_sha256":"a5a6525d08848846dd01e217e4b3fdfb9e26a816c395a8f5ff1dbbb1ac8d4631","abstract_canon_sha256":"f9e98bebd7ca3f8f1ce45c6fa80e9d8afff1092d446b2c4589ceda0d45f75ca5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:40:23.599298Z","signature_b64":"hwSjmMhhDRkFCYC0tUzUkkpwZisPoYZptqN5+UN6jzIUvU25wIzStm5UMctnwUOkGtRyfEpts5LCUDKXgNZjCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15591f5e7477be7e98768fe57ea3b293abfadd63d392c266245e0c079cb7c00d","last_reissued_at":"2026-07-05T02:40:23.598832Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:40:23.598832Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating the Robustness of Self-Supervised Learning in Medical Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anjany Sekuboyina, Bjoern H. Menze, Christopher Watanabe, Fernando Navarro, Jan C. Peeken, Stephanie E. Combs, Suprosanna Shit","submitted_at":"2021-05-14T17:49:52Z","abstract_excerpt":"Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to fully-supervised learning. Meanwhile, little attention has been given to study the robustness of networks trained in a self-supervised manner. In this work, we demonstrate that networks trained via self-supervised learning have superior robustness and generalizability compared t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.06986","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/2105.06986/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":"2105.06986","created_at":"2026-07-05T02:40:23.598890+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.06986v1","created_at":"2026-07-05T02:40:23.598890+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.06986","created_at":"2026-07-05T02:40:23.598890+00:00"},{"alias_kind":"pith_short_12","alias_value":"CVMR6XTUO67H","created_at":"2026-07-05T02:40:23.598890+00:00"},{"alias_kind":"pith_short_16","alias_value":"CVMR6XTUO67H5GDW","created_at":"2026-07-05T02:40:23.598890+00:00"},{"alias_kind":"pith_short_8","alias_value":"CVMR6XTU","created_at":"2026-07-05T02:40:23.598890+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.05755","citing_title":"An autonomous agent for auditing and improving the reliability of clinical AI models","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO","json":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO.json","graph_json":"https://pith.science/api/pith-number/CVMR6XTUO67H5GDWR7SX5I5SSO/graph.json","events_json":"https://pith.science/api/pith-number/CVMR6XTUO67H5GDWR7SX5I5SSO/events.json","paper":"https://pith.science/paper/CVMR6XTU"},"agent_actions":{"view_html":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO","download_json":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO.json","view_paper":"https://pith.science/paper/CVMR6XTU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.06986&json=true","fetch_graph":"https://pith.science/api/pith-number/CVMR6XTUO67H5GDWR7SX5I5SSO/graph.json","fetch_events":"https://pith.science/api/pith-number/CVMR6XTUO67H5GDWR7SX5I5SSO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO/action/storage_attestation","attest_author":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO/action/author_attestation","sign_citation":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO/action/citation_signature","submit_replication":"https://pith.science/pith/CVMR6XTUO67H5GDWR7SX5I5SSO/action/replication_record"}},"created_at":"2026-07-05T02:40:23.598890+00:00","updated_at":"2026-07-05T02:40:23.598890+00:00"}