{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KJOW6OCMD5HA4V6G2DDSOCMNC4","short_pith_number":"pith:KJOW6OCM","canonical_record":{"source":{"id":"2507.08460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T10:03:23Z","cross_cats_sorted":[],"title_canon_sha256":"6cf33256cc0bd47128f96d49cafea07c10be387102019e0ca7bd3aec6f6c0eab","abstract_canon_sha256":"8bdd984ebe4e7f9694c727b0181e64f1796206b8506ba30dd8788ee10b578e1d"},"schema_version":"1.0"},"canonical_sha256":"525d6f384c1f4e0e57c6d0c727098d1712e3606b046b1ee2cf5c4aa70b6734bf","source":{"kind":"arxiv","id":"2507.08460","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.08460","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"arxiv_version","alias_value":"2507.08460v1","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08460","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"pith_short_12","alias_value":"KJOW6OCMD5HA","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"pith_short_16","alias_value":"KJOW6OCMD5HA4V6G","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"pith_short_8","alias_value":"KJOW6OCM","created_at":"2026-07-05T11:35:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KJOW6OCMD5HA4V6G2DDSOCMNC4","target":"record","payload":{"canonical_record":{"source":{"id":"2507.08460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T10:03:23Z","cross_cats_sorted":[],"title_canon_sha256":"6cf33256cc0bd47128f96d49cafea07c10be387102019e0ca7bd3aec6f6c0eab","abstract_canon_sha256":"8bdd984ebe4e7f9694c727b0181e64f1796206b8506ba30dd8788ee10b578e1d"},"schema_version":"1.0"},"canonical_sha256":"525d6f384c1f4e0e57c6d0c727098d1712e3606b046b1ee2cf5c4aa70b6734bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:40.298302Z","signature_b64":"bsSr1dVIKw6pI+qJO4KErASlDqESSrxAtkXRo+WASIIINb0grZx2w1ckwOiIGyO0AjqqMFWlGd9DwFcFPsxhAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"525d6f384c1f4e0e57c6d0c727098d1712e3606b046b1ee2cf5c4aa70b6734bf","last_reissued_at":"2026-07-05T11:35:40.297838Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:40.297838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.08460","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":"2026-07-05T11:35:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sEn/OSYdgOBn7hsNdRXlUjnd54N4yU4qHvXW9XqicjJlffqUOV+wId30300BswY6/NavoaRWrsB+sjeoHkXhAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:19:37.692271Z"},"content_sha256":"e4ef61873a08d7f8498073ba4e0ddc6a2c46f30d797e9063b35834f05d0aee58","schema_version":"1.0","event_id":"sha256:e4ef61873a08d7f8498073ba4e0ddc6a2c46f30d797e9063b35834f05d0aee58"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KJOW6OCMD5HA4V6G2DDSOCMNC4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Reza Rahmanzadeh, Seyedeh Sahar Taheri Otaghsara","submitted_at":"2025-07-11T10:03:23Z","abstract_excerpt":"F3-Net is a foundation model designed to overcome persistent challenges in clinical medical image segmentation, including reliance on complete multimodal inputs, limited generalizability, and narrow task specificity. Through flexible synthetic modality training, F3-Net maintains robust performance even in the presence of missing MRI sequences, leveraging a zero-image strategy to substitute absent modalities without relying on explicit synthesis networks, thereby enhancing real-world applicability. Its unified architecture supports multi-pathology segmentation across glioma, metastasis, stroke,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08460","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/2507.08460/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":"2026-07-05T11:35:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zoPYGCUIOUFHVXo4Lu+DFH3E5BxaiiikSXl7OYCJWChoqmNXmlUXiA3CVrzjMvMKPhpQyIX2SSSzrJAJPHOKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:19:37.692884Z"},"content_sha256":"183b051f9ef43b229a24f083a70d616b5eec51d8f6c16f81b45fd0f721c189b4","schema_version":"1.0","event_id":"sha256:183b051f9ef43b229a24f083a70d616b5eec51d8f6c16f81b45fd0f721c189b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4/bundle.json","state_url":"https://pith.science/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4/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":"2026-08-07T01:19:37Z","links":{"resolver":"https://pith.science/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4","bundle":"https://pith.science/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4/bundle.json","state":"https://pith.science/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KJOW6OCMD5HA4V6G2DDSOCMNC4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KJOW6OCMD5HA4V6G2DDSOCMNC4","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":"8bdd984ebe4e7f9694c727b0181e64f1796206b8506ba30dd8788ee10b578e1d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T10:03:23Z","title_canon_sha256":"6cf33256cc0bd47128f96d49cafea07c10be387102019e0ca7bd3aec6f6c0eab"},"schema_version":"1.0","source":{"id":"2507.08460","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.08460","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"arxiv_version","alias_value":"2507.08460v1","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08460","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"pith_short_12","alias_value":"KJOW6OCMD5HA","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"pith_short_16","alias_value":"KJOW6OCMD5HA4V6G","created_at":"2026-07-05T11:35:40Z"},{"alias_kind":"pith_short_8","alias_value":"KJOW6OCM","created_at":"2026-07-05T11:35:40Z"}],"graph_snapshots":[{"event_id":"sha256:183b051f9ef43b229a24f083a70d616b5eec51d8f6c16f81b45fd0f721c189b4","target":"graph","created_at":"2026-07-05T11:35:40Z","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/2507.08460/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"F3-Net is a foundation model designed to overcome persistent challenges in clinical medical image segmentation, including reliance on complete multimodal inputs, limited generalizability, and narrow task specificity. Through flexible synthetic modality training, F3-Net maintains robust performance even in the presence of missing MRI sequences, leveraging a zero-image strategy to substitute absent modalities without relying on explicit synthesis networks, thereby enhancing real-world applicability. Its unified architecture supports multi-pathology segmentation across glioma, metastasis, stroke,","authors_text":"Reza Rahmanzadeh, Seyedeh Sahar Taheri Otaghsara","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T10:03:23Z","title":"F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08460","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:e4ef61873a08d7f8498073ba4e0ddc6a2c46f30d797e9063b35834f05d0aee58","target":"record","created_at":"2026-07-05T11:35:40Z","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":"8bdd984ebe4e7f9694c727b0181e64f1796206b8506ba30dd8788ee10b578e1d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T10:03:23Z","title_canon_sha256":"6cf33256cc0bd47128f96d49cafea07c10be387102019e0ca7bd3aec6f6c0eab"},"schema_version":"1.0","source":{"id":"2507.08460","kind":"arxiv","version":1}},"canonical_sha256":"525d6f384c1f4e0e57c6d0c727098d1712e3606b046b1ee2cf5c4aa70b6734bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"525d6f384c1f4e0e57c6d0c727098d1712e3606b046b1ee2cf5c4aa70b6734bf","first_computed_at":"2026-07-05T11:35:40.297838Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:35:40.297838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bsSr1dVIKw6pI+qJO4KErASlDqESSrxAtkXRo+WASIIINb0grZx2w1ckwOiIGyO0AjqqMFWlGd9DwFcFPsxhAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:35:40.298302Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.08460","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4ef61873a08d7f8498073ba4e0ddc6a2c46f30d797e9063b35834f05d0aee58","sha256:183b051f9ef43b229a24f083a70d616b5eec51d8f6c16f81b45fd0f721c189b4"],"state_sha256":"8f2ea25b225c3704e34e7b1c6899a88a4c79eb2fc60ae763e35f1f7e99f9cf47"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LGMYJjmg4XB+Gsie/l7pa0Pmz5Ys4EjBC3klLIT9d7fCeH4k3ayEcxyGBNcnqwOSyhDswfPtfMG0QSDlDD5GAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T01:19:37.696843Z","bundle_sha256":"e1061343468c0b06cbd95fb840c4dce958c1cbbb3c2abeae0fbcae3abd9a523e"}}