{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LUBTVYTHHWRJT6WYGGKMDCKZPB","short_pith_number":"pith:LUBTVYTH","schema_version":"1.0","canonical_sha256":"5d033ae2673da299fad83194c1895978635acb8df19954d775a321260ca03b58","source":{"kind":"arxiv","id":"2311.08239","version":2},"attestation_state":"computed","paper":{"title":"Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Anna Reithmeir, Julia A. Schnabel, Veronika A. Zimmer","submitted_at":"2023-11-14T15:20:42Z","abstract_excerpt":"Medical image registration aims at identifying the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To date, the majority of the deep learning-based registration methods employ regularizers that enforce global spatial smoothness, e.g., the diffusion regularizer. However, such regularizers are not tailored to the data and might not be capable of reflecting the complex underlying deformation. In contrast, physics-inspired regularizers promote physically plausible deformations. One such regularizer is the linear elastic re"},"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":"2311.08239","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-11-14T15:20:42Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d5048a18445ade4d359877e762579f6820a32a83707d44a17fff4368a4c795a1","abstract_canon_sha256":"13af80322798e3d243344e82d0aa5be5e8aeb21f8f6111d180e59122bbea3584"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:53.004893Z","signature_b64":"md/2twusTmDdIBplo94VskWciM63nsZ2TOyEDFKiDu60aY2FrpOOvgcehnbb8k4hzOnILGjQVZSSEb4izcVFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d033ae2673da299fad83194c1895978635acb8df19954d775a321260ca03b58","last_reissued_at":"2026-07-05T07:19:53.004405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:53.004405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Anna Reithmeir, Julia A. Schnabel, Veronika A. Zimmer","submitted_at":"2023-11-14T15:20:42Z","abstract_excerpt":"Medical image registration aims at identifying the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To date, the majority of the deep learning-based registration methods employ regularizers that enforce global spatial smoothness, e.g., the diffusion regularizer. However, such regularizers are not tailored to the data and might not be capable of reflecting the complex underlying deformation. In contrast, physics-inspired regularizers promote physically plausible deformations. One such regularizer is the linear elastic re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08239","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/2311.08239/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":"2311.08239","created_at":"2026-07-05T07:19:53.004467+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.08239v2","created_at":"2026-07-05T07:19:53.004467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08239","created_at":"2026-07-05T07:19:53.004467+00:00"},{"alias_kind":"pith_short_12","alias_value":"LUBTVYTHHWRJ","created_at":"2026-07-05T07:19:53.004467+00:00"},{"alias_kind":"pith_short_16","alias_value":"LUBTVYTHHWRJT6WY","created_at":"2026-07-05T07:19:53.004467+00:00"},{"alias_kind":"pith_short_8","alias_value":"LUBTVYTH","created_at":"2026-07-05T07:19:53.004467+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/LUBTVYTHHWRJT6WYGGKMDCKZPB","json":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB.json","graph_json":"https://pith.science/api/pith-number/LUBTVYTHHWRJT6WYGGKMDCKZPB/graph.json","events_json":"https://pith.science/api/pith-number/LUBTVYTHHWRJT6WYGGKMDCKZPB/events.json","paper":"https://pith.science/paper/LUBTVYTH"},"agent_actions":{"view_html":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB","download_json":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB.json","view_paper":"https://pith.science/paper/LUBTVYTH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.08239&json=true","fetch_graph":"https://pith.science/api/pith-number/LUBTVYTHHWRJT6WYGGKMDCKZPB/graph.json","fetch_events":"https://pith.science/api/pith-number/LUBTVYTHHWRJT6WYGGKMDCKZPB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB/action/storage_attestation","attest_author":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB/action/author_attestation","sign_citation":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB/action/citation_signature","submit_replication":"https://pith.science/pith/LUBTVYTHHWRJT6WYGGKMDCKZPB/action/replication_record"}},"created_at":"2026-07-05T07:19:53.004467+00:00","updated_at":"2026-07-05T07:19:53.004467+00:00"}