{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IBOMHD7RHR3O6YT4TVN4E3F7CV","short_pith_number":"pith:IBOMHD7R","schema_version":"1.0","canonical_sha256":"405cc38ff13c76ef627c9d5bc26cbf155370d34dbdb7b39b51d08e51c958c858","source":{"kind":"arxiv","id":"2509.09290","version":1},"attestation_state":"computed","paper":{"title":"Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anthony P. Addison, Felix Wagner, Konstantinos Kamnitsas, Natalie Voets, Wentian Xu","submitted_at":"2025-09-11T09:25:30Z","abstract_excerpt":"Segmentation models are important tools for the detection and analysis of lesions in brain MRI. Depending on the type of brain pathology that is imaged, MRI scanners can acquire multiple, different image modalities (contrasts). Most segmentation models for multimodal brain MRI are restricted to fixed modalities and cannot effectively process new ones at inference. Some models generalize to unseen modalities but may lose discriminative modality-specific information. This work aims to develop a model that can perform inference on data that contain image modalities unseen during training, previou"},"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":"2509.09290","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-11T09:25:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"838cbcebc10fe83258e1b4aecba3f1ce988d7b0a49486193c1ba6a0c2b8c1f40","abstract_canon_sha256":"77f9fded60e9de39a59643edf8724dc84a1a4b5f5610fb10011e029f1ac5b5e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:33.217830Z","signature_b64":"VpYpyT81PdMj0KDydIEJeZk+N4x8Y3zkQxUuK854QpK3bi6DlvfrdCr+ct/vedIMFzK7iI3PyzlXcsQKnmSmDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"405cc38ff13c76ef627c9d5bc26cbf155370d34dbdb7b39b51d08e51c958c858","last_reissued_at":"2026-07-05T12:09:33.217186Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:33.217186Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anthony P. Addison, Felix Wagner, Konstantinos Kamnitsas, Natalie Voets, Wentian Xu","submitted_at":"2025-09-11T09:25:30Z","abstract_excerpt":"Segmentation models are important tools for the detection and analysis of lesions in brain MRI. Depending on the type of brain pathology that is imaged, MRI scanners can acquire multiple, different image modalities (contrasts). Most segmentation models for multimodal brain MRI are restricted to fixed modalities and cannot effectively process new ones at inference. Some models generalize to unseen modalities but may lose discriminative modality-specific information. This work aims to develop a model that can perform inference on data that contain image modalities unseen during training, previou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09290","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/2509.09290/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":"2509.09290","created_at":"2026-07-05T12:09:33.217293+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.09290v1","created_at":"2026-07-05T12:09:33.217293+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09290","created_at":"2026-07-05T12:09:33.217293+00:00"},{"alias_kind":"pith_short_12","alias_value":"IBOMHD7RHR3O","created_at":"2026-07-05T12:09:33.217293+00:00"},{"alias_kind":"pith_short_16","alias_value":"IBOMHD7RHR3O6YT4","created_at":"2026-07-05T12:09:33.217293+00:00"},{"alias_kind":"pith_short_8","alias_value":"IBOMHD7R","created_at":"2026-07-05T12:09:33.217293+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/IBOMHD7RHR3O6YT4TVN4E3F7CV","json":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV.json","graph_json":"https://pith.science/api/pith-number/IBOMHD7RHR3O6YT4TVN4E3F7CV/graph.json","events_json":"https://pith.science/api/pith-number/IBOMHD7RHR3O6YT4TVN4E3F7CV/events.json","paper":"https://pith.science/paper/IBOMHD7R"},"agent_actions":{"view_html":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV","download_json":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV.json","view_paper":"https://pith.science/paper/IBOMHD7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.09290&json=true","fetch_graph":"https://pith.science/api/pith-number/IBOMHD7RHR3O6YT4TVN4E3F7CV/graph.json","fetch_events":"https://pith.science/api/pith-number/IBOMHD7RHR3O6YT4TVN4E3F7CV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV/action/storage_attestation","attest_author":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV/action/author_attestation","sign_citation":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV/action/citation_signature","submit_replication":"https://pith.science/pith/IBOMHD7RHR3O6YT4TVN4E3F7CV/action/replication_record"}},"created_at":"2026-07-05T12:09:33.217293+00:00","updated_at":"2026-07-05T12:09:33.217293+00:00"}