{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:DXC6OCMUJ6L4VLEAICWTUNMELP","short_pith_number":"pith:DXC6OCMU","schema_version":"1.0","canonical_sha256":"1dc5e709944f97caac8040ad3a35845bcccd5e993adf891864324f983dca921a","source":{"kind":"arxiv","id":"1711.01666","version":2},"attestation_state":"computed","paper":{"title":"Label-driven weakly-supervised learning for multimodal deformable image registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Caroline M. Moore, Dean C. Barratt, Eli Gibson, Ester Bonmati, J. Alison Noble, Marc Modat, Mark Emberton, Nooshin Ghavami, Tom Vercauteren, Yipeng Hu","submitted_at":"2017-11-05T22:01:57Z","abstract_excerpt":"Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in "},"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":"1711.01666","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-05T22:01:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bbc1ae22fa5319fd9455f82a805f4eed3a03e059ad1ac1bdb2f2ba0b0eb7bf93","abstract_canon_sha256":"1e7fde050bf01201df0f344150ff712dc324f69db0bd766c7980c12a4e3c2a5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:22.172770Z","signature_b64":"g+BVj7+0gL/oS4ficji5Te4DE1ZfsTMrcEBIbgKyclB0aWK96Awowsq5f58nunRtnYbFDNEJ6v8AK03GHD4yBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1dc5e709944f97caac8040ad3a35845bcccd5e993adf891864324f983dca921a","last_reissued_at":"2026-05-18T00:14:22.172140Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:22.172140Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Label-driven weakly-supervised learning for multimodal deformable image registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Caroline M. Moore, Dean C. Barratt, Eli Gibson, Ester Bonmati, J. Alison Noble, Marc Modat, Mark Emberton, Nooshin Ghavami, Tom Vercauteren, Yipeng Hu","submitted_at":"2017-11-05T22:01:57Z","abstract_excerpt":"Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01666","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":""},"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":"1711.01666","created_at":"2026-05-18T00:14:22.172221+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.01666v2","created_at":"2026-05-18T00:14:22.172221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01666","created_at":"2026-05-18T00:14:22.172221+00:00"},{"alias_kind":"pith_short_12","alias_value":"DXC6OCMUJ6L4","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"DXC6OCMUJ6L4VLEA","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"DXC6OCMU","created_at":"2026-05-18T12:31:12.930513+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/DXC6OCMUJ6L4VLEAICWTUNMELP","json":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP.json","graph_json":"https://pith.science/api/pith-number/DXC6OCMUJ6L4VLEAICWTUNMELP/graph.json","events_json":"https://pith.science/api/pith-number/DXC6OCMUJ6L4VLEAICWTUNMELP/events.json","paper":"https://pith.science/paper/DXC6OCMU"},"agent_actions":{"view_html":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP","download_json":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP.json","view_paper":"https://pith.science/paper/DXC6OCMU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.01666&json=true","fetch_graph":"https://pith.science/api/pith-number/DXC6OCMUJ6L4VLEAICWTUNMELP/graph.json","fetch_events":"https://pith.science/api/pith-number/DXC6OCMUJ6L4VLEAICWTUNMELP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP/action/storage_attestation","attest_author":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP/action/author_attestation","sign_citation":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP/action/citation_signature","submit_replication":"https://pith.science/pith/DXC6OCMUJ6L4VLEAICWTUNMELP/action/replication_record"}},"created_at":"2026-05-18T00:14:22.172221+00:00","updated_at":"2026-05-18T00:14:22.172221+00:00"}