{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HXJ35ZRFQMM4EEEPR626C3W3EE","short_pith_number":"pith:HXJ35ZRF","schema_version":"1.0","canonical_sha256":"3dd3bee6258319c2108f8fb5e16edb21102352555bb9e9829f1283ed9d76beb1","source":{"kind":"arxiv","id":"1711.06853","version":1},"attestation_state":"computed","paper":{"title":"DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ben Glocker, Bernhard Kainz, Daniel Rueckert, Martin Rajchl, Matthew C.H. Lee, Nick Pawlowski, Sofia Ira Ktena","submitted_at":"2017-11-18T12:31:10Z","abstract_excerpt":"We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data \"Multi-Atlas Labeling Beyond the Cranial Vault\". The average test Dice similarity coefficient"},"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":"1711.06853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-18T12:31:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"beac67c0563f1e99f807c6dfcdf6ce2ca772a53a5c856a972d4b27f173e0e5d7","abstract_canon_sha256":"2f701aa756cadc6ddc39eb9066c6f1cf7d9008da9df8ad9a8e3580bc2ac67edf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:15.355602Z","signature_b64":"+qOi9Tf7ojmEt+Q4hshfN3KbO2+1gFZrGeleBneEZ4RmvyVGKtf4yPpUsS4kXZlyZmh4k0B59/qZirqjSpypBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dd3bee6258319c2108f8fb5e16edb21102352555bb9e9829f1283ed9d76beb1","last_reissued_at":"2026-05-18T00:30:15.354880Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:15.354880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ben Glocker, Bernhard Kainz, Daniel Rueckert, Martin Rajchl, Matthew C.H. Lee, Nick Pawlowski, Sofia Ira Ktena","submitted_at":"2017-11-18T12:31:10Z","abstract_excerpt":"We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data \"Multi-Atlas Labeling Beyond the Cranial Vault\". The average test Dice similarity coefficient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06853","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":""},"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":"1711.06853","created_at":"2026-05-18T00:30:15.354994+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.06853v1","created_at":"2026-05-18T00:30:15.354994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06853","created_at":"2026-05-18T00:30:15.354994+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2211.02701","citing_title":"MONAI: An open-source framework for deep learning in healthcare","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE","json":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE.json","graph_json":"https://pith.science/api/pith-number/HXJ35ZRFQMM4EEEPR626C3W3EE/graph.json","events_json":"https://pith.science/api/pith-number/HXJ35ZRFQMM4EEEPR626C3W3EE/events.json","paper":"https://pith.science/paper/HXJ35ZRF"},"agent_actions":{"view_html":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE","download_json":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE.json","view_paper":"https://pith.science/paper/HXJ35ZRF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.06853&json=true","fetch_graph":"https://pith.science/api/pith-number/HXJ35ZRFQMM4EEEPR626C3W3EE/graph.json","fetch_events":"https://pith.science/api/pith-number/HXJ35ZRFQMM4EEEPR626C3W3EE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE/action/storage_attestation","attest_author":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE/action/author_attestation","sign_citation":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE/action/citation_signature","submit_replication":"https://pith.science/pith/HXJ35ZRFQMM4EEEPR626C3W3EE/action/replication_record"}},"created_at":"2026-05-18T00:30:15.354994+00:00","updated_at":"2026-05-18T00:30:15.354994+00:00"}