{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VSTFKK2CTAKNCMI22WUJEWWQHH","short_pith_number":"pith:VSTFKK2C","schema_version":"1.0","canonical_sha256":"aca6552b429814d1311ad5a8925ad039d395a249ccad01ad39f7bc68ec070279","source":{"kind":"arxiv","id":"2106.06637","version":1},"attestation_state":"computed","paper":{"title":"CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alejandro F Frangi, Nishant Ravikumar, Xiang Chen, Yan Xia","submitted_at":"2021-06-11T23:25:49Z","abstract_excerpt":"Image registration is a fundamental building block for various applications in medical image analysis. To better explore the correlation between the fixed and moving images and improve registration performance, we propose a novel deep learning network, Co-Attention guided Registration Network (CAR-Net). CAR-Net employs a co-attention block to learn a new representation of the inputs, which drives the registration of the fixed and moving images. Experiments on UK Biobank cardiac cine-magnetic resonance image data demonstrate that CAR-Net obtains higher registration accuracy and smoother deforma"},"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":"2106.06637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-06-11T23:25:49Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a88abed8b3f569a6b03a157f35761856890030ec9335a4646517e6172d057c42","abstract_canon_sha256":"5c7b586bd447be9f4d9c08181c61817e449b5f7d2f5be0b43a1aee456a6fd0ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:49:01.969970Z","signature_b64":"P/PGTUJRmfzZzLg5axRVoKzWZPpuiJLN7aoZURHmZAVtWEwtER+Y/I9oxW0RKsJ/dmd9YebgiZ1+ovK/ijH7BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aca6552b429814d1311ad5a8925ad039d395a249ccad01ad39f7bc68ec070279","last_reissued_at":"2026-07-05T02:49:01.969500Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:49:01.969500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alejandro F Frangi, Nishant Ravikumar, Xiang Chen, Yan Xia","submitted_at":"2021-06-11T23:25:49Z","abstract_excerpt":"Image registration is a fundamental building block for various applications in medical image analysis. To better explore the correlation between the fixed and moving images and improve registration performance, we propose a novel deep learning network, Co-Attention guided Registration Network (CAR-Net). CAR-Net employs a co-attention block to learn a new representation of the inputs, which drives the registration of the fixed and moving images. Experiments on UK Biobank cardiac cine-magnetic resonance image data demonstrate that CAR-Net obtains higher registration accuracy and smoother deforma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.06637","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/2106.06637/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":"2106.06637","created_at":"2026-07-05T02:49:01.969560+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.06637v1","created_at":"2026-07-05T02:49:01.969560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.06637","created_at":"2026-07-05T02:49:01.969560+00:00"},{"alias_kind":"pith_short_12","alias_value":"VSTFKK2CTAKN","created_at":"2026-07-05T02:49:01.969560+00:00"},{"alias_kind":"pith_short_16","alias_value":"VSTFKK2CTAKNCMI2","created_at":"2026-07-05T02:49:01.969560+00:00"},{"alias_kind":"pith_short_8","alias_value":"VSTFKK2C","created_at":"2026-07-05T02:49:01.969560+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.24160","citing_title":"Beyond the LUMIR challenge: The pathway to foundational registration models","ref_index":64,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH","json":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH.json","graph_json":"https://pith.science/api/pith-number/VSTFKK2CTAKNCMI22WUJEWWQHH/graph.json","events_json":"https://pith.science/api/pith-number/VSTFKK2CTAKNCMI22WUJEWWQHH/events.json","paper":"https://pith.science/paper/VSTFKK2C"},"agent_actions":{"view_html":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH","download_json":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH.json","view_paper":"https://pith.science/paper/VSTFKK2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.06637&json=true","fetch_graph":"https://pith.science/api/pith-number/VSTFKK2CTAKNCMI22WUJEWWQHH/graph.json","fetch_events":"https://pith.science/api/pith-number/VSTFKK2CTAKNCMI22WUJEWWQHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH/action/storage_attestation","attest_author":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH/action/author_attestation","sign_citation":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH/action/citation_signature","submit_replication":"https://pith.science/pith/VSTFKK2CTAKNCMI22WUJEWWQHH/action/replication_record"}},"created_at":"2026-07-05T02:49:01.969560+00:00","updated_at":"2026-07-05T02:49:01.969560+00:00"}