{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KA3PSSL2Q6OT4DVMCG5QJ2R6E6","short_pith_number":"pith:KA3PSSL2","canonical_record":{"source":{"id":"2504.13645","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T11:52:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2ae8465cf7a34c59457e3e3c732822a49c19cad0597082ba3c7751a4b26515ba","abstract_canon_sha256":"7356bb53cb397bedc32ce8a9ac1100ffec3a8a8ea9e2debab137f2367c5d0b9c"},"schema_version":"1.0"},"canonical_sha256":"5036f9497a879d3e0eac11bb04ea3e2799c1622beb1cffed9eb677f0109c6e13","source":{"kind":"arxiv","id":"2504.13645","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13645","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13645v1","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13645","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_12","alias_value":"KA3PSSL2Q6OT","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_16","alias_value":"KA3PSSL2Q6OT4DVM","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_8","alias_value":"KA3PSSL2","created_at":"2026-07-05T10:50:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KA3PSSL2Q6OT4DVMCG5QJ2R6E6","target":"record","payload":{"canonical_record":{"source":{"id":"2504.13645","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T11:52:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2ae8465cf7a34c59457e3e3c732822a49c19cad0597082ba3c7751a4b26515ba","abstract_canon_sha256":"7356bb53cb397bedc32ce8a9ac1100ffec3a8a8ea9e2debab137f2367c5d0b9c"},"schema_version":"1.0"},"canonical_sha256":"5036f9497a879d3e0eac11bb04ea3e2799c1622beb1cffed9eb677f0109c6e13","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:59.701375Z","signature_b64":"XHMPBHOOB3T/5MXCY6uerDAUr4woEMeIS30RQR4v0+nJg4Hkl9Cv4GGyxxr1KTkIS9zrdSOc6hPDym5Zj/ZSBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5036f9497a879d3e0eac11bb04ea3e2799c1622beb1cffed9eb677f0109c6e13","last_reissued_at":"2026-07-05T10:50:59.700863Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:59.700863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.13645","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sx6LRf8X+S0HYCHc9EYuJqkSaXFoFCPXaQrPxO8JuN5vXI6Qdu5PO2QSvuxWCSw4iEZoKJP51fD5BbrLRSEzCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:30:23.738351Z"},"content_sha256":"080155496ab0d684c33acf348c3bae66ac66a64b58941e9f95fb14d6d5937714","schema_version":"1.0","event_id":"sha256:080155496ab0d684c33acf348c3bae66ac66a64b58941e9f95fb14d6d5937714"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KA3PSSL2Q6OT4DVMCG5QJ2R6E6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Karthik Nandakumar, Mohammad Yaqub, Muhammad Ridzuan, Nada Saadi, Numan Saeed, Shahad Hardan","submitted_at":"2025-04-18T11:52:21Z","abstract_excerpt":"Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing information from these modalities. However, a critical bottleneck exists: the dependency on CT-PET data concurrently for training and inference, posing a challenge due to the limited availability of PET scans. Hence, there is a clear need for a flexible and efficient framework that can be trained with the widely available CT scans and can be still adapted for PET scans when they become available. In this work, we propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13645","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/2504.13645/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZuGmJmGauFWmJ0BAtHyQhykDHO/lOLn+8zIy482Z07VMWZFsu6JxlyaluS2fkdZ1w08JZjBBWLNfXpEMzdk8DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:30:23.738727Z"},"content_sha256":"0b72cbcd78fbf4cde12f57347641bbc3cd47b8ecb6c15f4b1c2ffe3f529ab654","schema_version":"1.0","event_id":"sha256:0b72cbcd78fbf4cde12f57347641bbc3cd47b8ecb6c15f4b1c2ffe3f529ab654"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6/bundle.json","state_url":"https://pith.science/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T08:30:23Z","links":{"resolver":"https://pith.science/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6","bundle":"https://pith.science/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6/bundle.json","state":"https://pith.science/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KA3PSSL2Q6OT4DVMCG5QJ2R6E6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KA3PSSL2Q6OT4DVMCG5QJ2R6E6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7356bb53cb397bedc32ce8a9ac1100ffec3a8a8ea9e2debab137f2367c5d0b9c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T11:52:21Z","title_canon_sha256":"2ae8465cf7a34c59457e3e3c732822a49c19cad0597082ba3c7751a4b26515ba"},"schema_version":"1.0","source":{"id":"2504.13645","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13645","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13645v1","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13645","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_12","alias_value":"KA3PSSL2Q6OT","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_16","alias_value":"KA3PSSL2Q6OT4DVM","created_at":"2026-07-05T10:50:59Z"},{"alias_kind":"pith_short_8","alias_value":"KA3PSSL2","created_at":"2026-07-05T10:50:59Z"}],"graph_snapshots":[{"event_id":"sha256:0b72cbcd78fbf4cde12f57347641bbc3cd47b8ecb6c15f4b1c2ffe3f529ab654","target":"graph","created_at":"2026-07-05T10:50:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.13645/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing information from these modalities. However, a critical bottleneck exists: the dependency on CT-PET data concurrently for training and inference, posing a challenge due to the limited availability of PET scans. Hence, there is a clear need for a flexible and efficient framework that can be trained with the widely available CT scans and can be still adapted for PET scans when they become available. In this work, we propose","authors_text":"Karthik Nandakumar, Mohammad Yaqub, Muhammad Ridzuan, Nada Saadi, Numan Saeed, Shahad Hardan","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T11:52:21Z","title":"Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13645","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:080155496ab0d684c33acf348c3bae66ac66a64b58941e9f95fb14d6d5937714","target":"record","created_at":"2026-07-05T10:50:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7356bb53cb397bedc32ce8a9ac1100ffec3a8a8ea9e2debab137f2367c5d0b9c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T11:52:21Z","title_canon_sha256":"2ae8465cf7a34c59457e3e3c732822a49c19cad0597082ba3c7751a4b26515ba"},"schema_version":"1.0","source":{"id":"2504.13645","kind":"arxiv","version":1}},"canonical_sha256":"5036f9497a879d3e0eac11bb04ea3e2799c1622beb1cffed9eb677f0109c6e13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5036f9497a879d3e0eac11bb04ea3e2799c1622beb1cffed9eb677f0109c6e13","first_computed_at":"2026-07-05T10:50:59.700863Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:59.700863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XHMPBHOOB3T/5MXCY6uerDAUr4woEMeIS30RQR4v0+nJg4Hkl9Cv4GGyxxr1KTkIS9zrdSOc6hPDym5Zj/ZSBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:59.701375Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.13645","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:080155496ab0d684c33acf348c3bae66ac66a64b58941e9f95fb14d6d5937714","sha256:0b72cbcd78fbf4cde12f57347641bbc3cd47b8ecb6c15f4b1c2ffe3f529ab654"],"state_sha256":"441be93bcbed3eea2693621daf187f8950c7269f98de8201a8b6844018b1c38f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G2Wacy6kqCh2fvDE0C6yaHi4FYhNCmCTMH+IbAQnakwCWzbiIpD/5rp872JKV95+Ecq9XSLoI/Mx9mFC3HhvBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:30:23.740797Z","bundle_sha256":"ac4f694ca1998f9676904c4688c22e8959b258cb7ca30f60e8652a81bcc83153"}}