{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:3UPXIXOCY5CZUMEQ2IBKWGU6FF","short_pith_number":"pith:3UPXIXOC","schema_version":"1.0","canonical_sha256":"dd1f745dc2c7459a3090d202ab1a9e294f291667490f70a3edd366e00b0602bc","source":{"kind":"arxiv","id":"2102.04938","version":1},"attestation_state":"computed","paper":{"title":"Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Anneke Meyer, Christian Hansen, Daniel Schindele, Klaus T\\\"onnies, Marko Rak, Martin Schostak, Oleksii Bashkanov","submitted_at":"2021-02-09T16:48:59Z","abstract_excerpt":"Recent studies demonstrated the eligibility of convolutional neural networks (CNNs) for solving the image registration problem. CNNs enable faster transformation estimation and greater generalization capability needed for better support during medical interventions. Conventional fully-supervised training requires a lot of high-quality ground truth data such as voxel-to-voxel transformations, which typically are attained in a too tedious and error-prone manner. In our work, we use weakly-supervised learning, which optimizes the model indirectly only via segmentation masks that are a more access"},"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":"2102.04938","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-02-09T16:48:59Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"6ce217cb432bfb02106e7fc8b7096278e54148831067cd2e57007bd8d529d44a","abstract_canon_sha256":"07b7c3e9dbcbdf93c12fd6fba4b4e0c5c45658c81ed8b442afd8d053cd3d30dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:14:06.482902Z","signature_b64":"nY/B/5OW+l+4DwCsATFZdl/vB9bcZt6185eWa7hbr6Dkg0Db4pJgQGitMxrik5/uo0QK7V+CXkvg+3yX7MRrDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd1f745dc2c7459a3090d202ab1a9e294f291667490f70a3edd366e00b0602bc","last_reissued_at":"2026-07-05T02:14:06.482469Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:14:06.482469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Anneke Meyer, Christian Hansen, Daniel Schindele, Klaus T\\\"onnies, Marko Rak, Martin Schostak, Oleksii Bashkanov","submitted_at":"2021-02-09T16:48:59Z","abstract_excerpt":"Recent studies demonstrated the eligibility of convolutional neural networks (CNNs) for solving the image registration problem. CNNs enable faster transformation estimation and greater generalization capability needed for better support during medical interventions. Conventional fully-supervised training requires a lot of high-quality ground truth data such as voxel-to-voxel transformations, which typically are attained in a too tedious and error-prone manner. In our work, we use weakly-supervised learning, which optimizes the model indirectly only via segmentation masks that are a more access"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.04938","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/2102.04938/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":"2102.04938","created_at":"2026-07-05T02:14:06.482531+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.04938v1","created_at":"2026-07-05T02:14:06.482531+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.04938","created_at":"2026-07-05T02:14:06.482531+00:00"},{"alias_kind":"pith_short_12","alias_value":"3UPXIXOCY5CZ","created_at":"2026-07-05T02:14:06.482531+00:00"},{"alias_kind":"pith_short_16","alias_value":"3UPXIXOCY5CZUMEQ","created_at":"2026-07-05T02:14:06.482531+00:00"},{"alias_kind":"pith_short_8","alias_value":"3UPXIXOC","created_at":"2026-07-05T02:14:06.482531+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/3UPXIXOCY5CZUMEQ2IBKWGU6FF","json":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF.json","graph_json":"https://pith.science/api/pith-number/3UPXIXOCY5CZUMEQ2IBKWGU6FF/graph.json","events_json":"https://pith.science/api/pith-number/3UPXIXOCY5CZUMEQ2IBKWGU6FF/events.json","paper":"https://pith.science/paper/3UPXIXOC"},"agent_actions":{"view_html":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF","download_json":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF.json","view_paper":"https://pith.science/paper/3UPXIXOC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.04938&json=true","fetch_graph":"https://pith.science/api/pith-number/3UPXIXOCY5CZUMEQ2IBKWGU6FF/graph.json","fetch_events":"https://pith.science/api/pith-number/3UPXIXOCY5CZUMEQ2IBKWGU6FF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF/action/storage_attestation","attest_author":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF/action/author_attestation","sign_citation":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF/action/citation_signature","submit_replication":"https://pith.science/pith/3UPXIXOCY5CZUMEQ2IBKWGU6FF/action/replication_record"}},"created_at":"2026-07-05T02:14:06.482531+00:00","updated_at":"2026-07-05T02:14:06.482531+00:00"}