{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GMR2QSNNW7XP2WC3NYXVEY43QB","short_pith_number":"pith:GMR2QSNN","canonical_record":{"source":{"id":"2304.10864","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-21T10:23:34Z","cross_cats_sorted":[],"title_canon_sha256":"d60599f4b334abfc0d614800cf83b398f26e81abca9540d53dcf526a252a62eb","abstract_canon_sha256":"0d69be6b73027956fe6cd5ca59dd63f2605f7c264f5b01ca8daec74b54b2ea5b"},"schema_version":"1.0"},"canonical_sha256":"3323a849adb7eefd585b6e2f52639b80560b067f561f119ec7e200dd1978cca3","source":{"kind":"arxiv","id":"2304.10864","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.10864","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"arxiv_version","alias_value":"2304.10864v3","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.10864","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"pith_short_12","alias_value":"GMR2QSNNW7XP","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"pith_short_16","alias_value":"GMR2QSNNW7XP2WC3","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"pith_short_8","alias_value":"GMR2QSNN","created_at":"2026-07-05T07:18:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GMR2QSNNW7XP2WC3NYXVEY43QB","target":"record","payload":{"canonical_record":{"source":{"id":"2304.10864","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-21T10:23:34Z","cross_cats_sorted":[],"title_canon_sha256":"d60599f4b334abfc0d614800cf83b398f26e81abca9540d53dcf526a252a62eb","abstract_canon_sha256":"0d69be6b73027956fe6cd5ca59dd63f2605f7c264f5b01ca8daec74b54b2ea5b"},"schema_version":"1.0"},"canonical_sha256":"3323a849adb7eefd585b6e2f52639b80560b067f561f119ec7e200dd1978cca3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:18:26.252967Z","signature_b64":"g+gJFifqSdIIfZBM7QNhGLDihmA1sUsYrv7Rv3klqVH388RU4IQWZiUu67YMSEoMULSvlPwTPgqDvUy76vmcBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3323a849adb7eefd585b6e2f52639b80560b067f561f119ec7e200dd1978cca3","last_reissued_at":"2026-07-05T07:18:26.252511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:18:26.252511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.10864","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:18:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XItERIUA9LQ09+CZgH1jSBhzjrbpkus1Zqwtd4IJoOHFixTOGQoWQ3C6q+lN9qYJJfnjvv3RkWPzqFwnlr4KAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T04:54:00.553875Z"},"content_sha256":"2268c7c3f1980241d93d3bbf49514051d006a92936cfa958c4183ede2b176561","schema_version":"1.0","event_id":"sha256:2268c7c3f1980241d93d3bbf49514051d006a92936cfa958c4183ede2b176561"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GMR2QSNNW7XP2WC3NYXVEY43QB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Chen, Jianbo Jiao, Jiangyun Li, Jing Wang, Shanshan Song, Wenxuan Wang, Yuanxiu Cai","submitted_at":"2023-04-21T10:23:34Z","abstract_excerpt":"The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMIM for self-supervised pre-training to better accomplish medical image segmentation tasks. Based on the observations that the detailed structural information mainly lies in the high-fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.10864","kind":"arxiv","version":3},"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/2304.10864/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":"2026-07-05T07:18:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HbpnsQBqP07Mj7HdipkLpmWkX0h7AGTSmnP94t4gjQaoSl10Rnzdq6Moh3M2t2qIVI6Txrm2QANmkkSZxZ4LCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T04:54:00.554254Z"},"content_sha256":"ebfd15a958bb2367c43c29e040948b68a12961c0c19f6361cda8ddd7de72144b","schema_version":"1.0","event_id":"sha256:ebfd15a958bb2367c43c29e040948b68a12961c0c19f6361cda8ddd7de72144b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMR2QSNNW7XP2WC3NYXVEY43QB/bundle.json","state_url":"https://pith.science/pith/GMR2QSNNW7XP2WC3NYXVEY43QB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMR2QSNNW7XP2WC3NYXVEY43QB/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":"2026-08-20T04:54:00Z","links":{"resolver":"https://pith.science/pith/GMR2QSNNW7XP2WC3NYXVEY43QB","bundle":"https://pith.science/pith/GMR2QSNNW7XP2WC3NYXVEY43QB/bundle.json","state":"https://pith.science/pith/GMR2QSNNW7XP2WC3NYXVEY43QB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMR2QSNNW7XP2WC3NYXVEY43QB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GMR2QSNNW7XP2WC3NYXVEY43QB","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":"0d69be6b73027956fe6cd5ca59dd63f2605f7c264f5b01ca8daec74b54b2ea5b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-21T10:23:34Z","title_canon_sha256":"d60599f4b334abfc0d614800cf83b398f26e81abca9540d53dcf526a252a62eb"},"schema_version":"1.0","source":{"id":"2304.10864","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.10864","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"arxiv_version","alias_value":"2304.10864v3","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.10864","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"pith_short_12","alias_value":"GMR2QSNNW7XP","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"pith_short_16","alias_value":"GMR2QSNNW7XP2WC3","created_at":"2026-07-05T07:18:26Z"},{"alias_kind":"pith_short_8","alias_value":"GMR2QSNN","created_at":"2026-07-05T07:18:26Z"}],"graph_snapshots":[{"event_id":"sha256:ebfd15a958bb2367c43c29e040948b68a12961c0c19f6361cda8ddd7de72144b","target":"graph","created_at":"2026-07-05T07:18:26Z","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/2304.10864/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMIM for self-supervised pre-training to better accomplish medical image segmentation tasks. Based on the observations that the detailed structural information mainly lies in the high-fr","authors_text":"Chen Chen, Jianbo Jiao, Jiangyun Li, Jing Wang, Shanshan Song, Wenxuan Wang, Yuanxiu Cai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-21T10:23:34Z","title":"FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.10864","kind":"arxiv","version":3},"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":"sha256:2268c7c3f1980241d93d3bbf49514051d006a92936cfa958c4183ede2b176561","target":"record","created_at":"2026-07-05T07:18:26Z","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":"0d69be6b73027956fe6cd5ca59dd63f2605f7c264f5b01ca8daec74b54b2ea5b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-21T10:23:34Z","title_canon_sha256":"d60599f4b334abfc0d614800cf83b398f26e81abca9540d53dcf526a252a62eb"},"schema_version":"1.0","source":{"id":"2304.10864","kind":"arxiv","version":3}},"canonical_sha256":"3323a849adb7eefd585b6e2f52639b80560b067f561f119ec7e200dd1978cca3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3323a849adb7eefd585b6e2f52639b80560b067f561f119ec7e200dd1978cca3","first_computed_at":"2026-07-05T07:18:26.252511Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:18:26.252511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g+gJFifqSdIIfZBM7QNhGLDihmA1sUsYrv7Rv3klqVH388RU4IQWZiUu67YMSEoMULSvlPwTPgqDvUy76vmcBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:18:26.252967Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.10864","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2268c7c3f1980241d93d3bbf49514051d006a92936cfa958c4183ede2b176561","sha256:ebfd15a958bb2367c43c29e040948b68a12961c0c19f6361cda8ddd7de72144b"],"state_sha256":"a9392b4d09995ed7f637447e03ba365dac7a84f6bca0c3aff7b60de0eaae8469"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z38vhg+xswIxznOI4KMo/Xb5bnsLcCSr1xy3M2fwojWInQSc9fvmxVHhewgNoIK8ZB1PsZCePl5Bkprg6VjBCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T04:54:00.556637Z","bundle_sha256":"98e8221a8518bd565c0688b6ddd10d516d133a548a56aa3278eb82be67a3f473"}}