{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3EU4MWRCBZPNMLURY7ZVNY2LHF","short_pith_number":"pith:3EU4MWRC","schema_version":"1.0","canonical_sha256":"d929c65a220e5ed62e91c7f356e34b395c050e452c51b491b25eedf4eeda3240","source":{"kind":"arxiv","id":"2408.11733","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Cross-Modal Medical Image Segmentation through Compositionality","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aniek Eijpe, Kalina Chupetlovska, Regina Beets-Tan, Valentina Corbetta, Wilson Silva","submitted_at":"2024-08-21T15:57:24Z","abstract_excerpt":"Cross-modal medical image segmentation presents a significant challenge, as different imaging modalities produce images with varying resolutions, contrasts, and appearances of anatomical structures. We introduce compositionality as an inductive bias in a cross-modal segmentation network to improve segmentation performance and interpretability while reducing complexity. The proposed network is an end-to-end cross-modal segmentation framework that enforces compositionality on the learned representations using learnable von Mises-Fisher kernels. These kernels facilitate content-style disentanglem"},"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":"2408.11733","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-21T15:57:24Z","cross_cats_sorted":[],"title_canon_sha256":"187aaabb016baf160707789d8f192adfaa2361c05fc76d83650b9c36148dfd00","abstract_canon_sha256":"03d2b11bfd3b56fb30a4d5a5d57a99e71cd9cd72066fc41dcc8d005f06c2a2fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:48.233491Z","signature_b64":"vYuCzS4fn86b3D2rONE7CvYYgtCnyq9XvlgkSCEaVGGUkOho5qIMaoFFzVwP8P/yfnkugTO99w7vXvYUXxhyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d929c65a220e5ed62e91c7f356e34b395c050e452c51b491b25eedf4eeda3240","last_reissued_at":"2026-07-05T08:57:48.233044Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:48.233044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Cross-Modal Medical Image Segmentation through Compositionality","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aniek Eijpe, Kalina Chupetlovska, Regina Beets-Tan, Valentina Corbetta, Wilson Silva","submitted_at":"2024-08-21T15:57:24Z","abstract_excerpt":"Cross-modal medical image segmentation presents a significant challenge, as different imaging modalities produce images with varying resolutions, contrasts, and appearances of anatomical structures. We introduce compositionality as an inductive bias in a cross-modal segmentation network to improve segmentation performance and interpretability while reducing complexity. The proposed network is an end-to-end cross-modal segmentation framework that enforces compositionality on the learned representations using learnable von Mises-Fisher kernels. These kernels facilitate content-style disentanglem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11733","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/2408.11733/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":"2408.11733","created_at":"2026-07-05T08:57:48.233103+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.11733v1","created_at":"2026-07-05T08:57:48.233103+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11733","created_at":"2026-07-05T08:57:48.233103+00:00"},{"alias_kind":"pith_short_12","alias_value":"3EU4MWRCBZPN","created_at":"2026-07-05T08:57:48.233103+00:00"},{"alias_kind":"pith_short_16","alias_value":"3EU4MWRCBZPNMLUR","created_at":"2026-07-05T08:57:48.233103+00:00"},{"alias_kind":"pith_short_8","alias_value":"3EU4MWRC","created_at":"2026-07-05T08:57:48.233103+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/3EU4MWRCBZPNMLURY7ZVNY2LHF","json":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF.json","graph_json":"https://pith.science/api/pith-number/3EU4MWRCBZPNMLURY7ZVNY2LHF/graph.json","events_json":"https://pith.science/api/pith-number/3EU4MWRCBZPNMLURY7ZVNY2LHF/events.json","paper":"https://pith.science/paper/3EU4MWRC"},"agent_actions":{"view_html":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF","download_json":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF.json","view_paper":"https://pith.science/paper/3EU4MWRC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.11733&json=true","fetch_graph":"https://pith.science/api/pith-number/3EU4MWRCBZPNMLURY7ZVNY2LHF/graph.json","fetch_events":"https://pith.science/api/pith-number/3EU4MWRCBZPNMLURY7ZVNY2LHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF/action/storage_attestation","attest_author":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF/action/author_attestation","sign_citation":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF/action/citation_signature","submit_replication":"https://pith.science/pith/3EU4MWRCBZPNMLURY7ZVNY2LHF/action/replication_record"}},"created_at":"2026-07-05T08:57:48.233103+00:00","updated_at":"2026-07-05T08:57:48.233103+00:00"}