{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4H3ICGXBDYXVPAL6OYOZTJHINE","short_pith_number":"pith:4H3ICGXB","schema_version":"1.0","canonical_sha256":"e1f6811ae11e2f57817e761d99a4e86935cfc90749ace13adfe986061ed9f52c","source":{"kind":"arxiv","id":"2211.11687","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Herve Delingette, Maxime Sermesant, Yingyu Yang, Zihao Wang","submitted_at":"2022-11-21T17:59:04Z","abstract_excerpt":"Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity. In recent years, convolutional neural network (CNN) based image registration methods have shown good effectiveness. In the meantime, recent studies show that the attention-based model (e.g., Transformer) can bring superior performance in p"},"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":"2211.11687","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-21T17:59:04Z","cross_cats_sorted":[],"title_canon_sha256":"67389db10a49d6cbb37ee8f3e38a4532f7b1a44f5bea3f7acae20677510e4e0e","abstract_canon_sha256":"8417f25f974a2c1ec2313f5bcf2eb7154c92a9af2af9eb0676b6895d7139e154"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:17:56.870245Z","signature_b64":"9LMRdgcb48RyKk8zZNX6TRM6jLOWWcYgSz3W2Rw5rZZcmOq61xvealghFVASH1sdGNdFtm0a2SZm4H5pb9vADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1f6811ae11e2f57817e761d99a4e86935cfc90749ace13adfe986061ed9f52c","last_reissued_at":"2026-07-05T05:17:56.869885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:17:56.869885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Herve Delingette, Maxime Sermesant, Yingyu Yang, Zihao Wang","submitted_at":"2022-11-21T17:59:04Z","abstract_excerpt":"Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity. In recent years, convolutional neural network (CNN) based image registration methods have shown good effectiveness. In the meantime, recent studies show that the attention-based model (e.g., Transformer) can bring superior performance in p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.11687","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/2211.11687/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":"2211.11687","created_at":"2026-07-05T05:17:56.869949+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.11687v1","created_at":"2026-07-05T05:17:56.869949+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.11687","created_at":"2026-07-05T05:17:56.869949+00:00"},{"alias_kind":"pith_short_12","alias_value":"4H3ICGXBDYXV","created_at":"2026-07-05T05:17:56.869949+00:00"},{"alias_kind":"pith_short_16","alias_value":"4H3ICGXBDYXVPAL6","created_at":"2026-07-05T05:17:56.869949+00:00"},{"alias_kind":"pith_short_8","alias_value":"4H3ICGXB","created_at":"2026-07-05T05:17:56.869949+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/4H3ICGXBDYXVPAL6OYOZTJHINE","json":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE.json","graph_json":"https://pith.science/api/pith-number/4H3ICGXBDYXVPAL6OYOZTJHINE/graph.json","events_json":"https://pith.science/api/pith-number/4H3ICGXBDYXVPAL6OYOZTJHINE/events.json","paper":"https://pith.science/paper/4H3ICGXB"},"agent_actions":{"view_html":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE","download_json":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE.json","view_paper":"https://pith.science/paper/4H3ICGXB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.11687&json=true","fetch_graph":"https://pith.science/api/pith-number/4H3ICGXBDYXVPAL6OYOZTJHINE/graph.json","fetch_events":"https://pith.science/api/pith-number/4H3ICGXBDYXVPAL6OYOZTJHINE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE/action/storage_attestation","attest_author":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE/action/author_attestation","sign_citation":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE/action/citation_signature","submit_replication":"https://pith.science/pith/4H3ICGXBDYXVPAL6OYOZTJHINE/action/replication_record"}},"created_at":"2026-07-05T05:17:56.869949+00:00","updated_at":"2026-07-05T05:17:56.869949+00:00"}