{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MXWMEQHFKJXJDEX3YUCNGIHLWK","short_pith_number":"pith:MXWMEQHF","canonical_record":{"source":{"id":"2404.10156","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-15T22:12:05Z","cross_cats_sorted":[],"title_canon_sha256":"7940bdbc55045d4eb75e1aa3bb77c647d3b30a4ab110abae22d88c6b0231ab45","abstract_canon_sha256":"4e7112d5c46a10ca7f6189cfc429ee6c60fd1ebfa0469a7059150b58f4e61fbb"},"schema_version":"1.0"},"canonical_sha256":"65ecc240e5526e9192fbc504d320ebb28d26e13d601342756fb04125c69b0875","source":{"kind":"arxiv","id":"2404.10156","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.10156","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"arxiv_version","alias_value":"2404.10156v2","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10156","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"pith_short_12","alias_value":"MXWMEQHFKJXJ","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"pith_short_16","alias_value":"MXWMEQHFKJXJDEX3","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"pith_short_8","alias_value":"MXWMEQHF","created_at":"2026-07-05T08:11:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MXWMEQHFKJXJDEX3YUCNGIHLWK","target":"record","payload":{"canonical_record":{"source":{"id":"2404.10156","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-15T22:12:05Z","cross_cats_sorted":[],"title_canon_sha256":"7940bdbc55045d4eb75e1aa3bb77c647d3b30a4ab110abae22d88c6b0231ab45","abstract_canon_sha256":"4e7112d5c46a10ca7f6189cfc429ee6c60fd1ebfa0469a7059150b58f4e61fbb"},"schema_version":"1.0"},"canonical_sha256":"65ecc240e5526e9192fbc504d320ebb28d26e13d601342756fb04125c69b0875","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:24.233764Z","signature_b64":"ibCru+K5aCi6mRflZ1lVtRIo9YbODUAlOErYD6mDK5ORXXSldU5Eq6gyM8LhgMi2SpzRBEJnvNej2MM47YyLBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65ecc240e5526e9192fbc504d320ebb28d26e13d601342756fb04125c69b0875","last_reissued_at":"2026-07-05T08:11:24.233281Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:24.233281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.10156","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:11:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sE+TjdcZ/j8b3sqIcPE82e2HixWr+mcBAKF8xze0MWbdjaQIxEQiIoqexVwUEwOvymDooyUO1evAyJt2mA3yAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:47:03.475765Z"},"content_sha256":"d17849a0aaf2d08b43cad9434a5edff620bcd93c98576602683e79e33a3c9a15","schema_version":"1.0","event_id":"sha256:d17849a0aaf2d08b43cad9434a5edff620bcd93c98576602683e79e33a3c9a15"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MXWMEQHFKJXJDEX3YUCNGIHLWK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alper Yilmaz, Pouyan Navard, Shehan Perera","submitted_at":"2024-04-15T22:12:05Z","abstract_excerpt":"The adoption of Vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10156","kind":"arxiv","version":2},"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/2404.10156/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":"2026-07-05T08:11:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kDtbgO7XjFUGbcZy0k5Sca8qKuCVLTrKcux/XX0x/AqQmhAm56EE344zfBGIqFv2UeuOn+PjGed+TGrwMH/HDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:47:03.476303Z"},"content_sha256":"a98936b191ed02222a5ea22f6568ff0b9bab9766b5267fad979719c60ebfbfe6","schema_version":"1.0","event_id":"sha256:a98936b191ed02222a5ea22f6568ff0b9bab9766b5267fad979719c60ebfbfe6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK/bundle.json","state_url":"https://pith.science/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK/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":"2026-08-18T21:47:03Z","links":{"resolver":"https://pith.science/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK","bundle":"https://pith.science/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK/bundle.json","state":"https://pith.science/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MXWMEQHFKJXJDEX3YUCNGIHLWK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MXWMEQHFKJXJDEX3YUCNGIHLWK","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":"4e7112d5c46a10ca7f6189cfc429ee6c60fd1ebfa0469a7059150b58f4e61fbb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-15T22:12:05Z","title_canon_sha256":"7940bdbc55045d4eb75e1aa3bb77c647d3b30a4ab110abae22d88c6b0231ab45"},"schema_version":"1.0","source":{"id":"2404.10156","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.10156","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"arxiv_version","alias_value":"2404.10156v2","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10156","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"pith_short_12","alias_value":"MXWMEQHFKJXJ","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"pith_short_16","alias_value":"MXWMEQHFKJXJDEX3","created_at":"2026-07-05T08:11:24Z"},{"alias_kind":"pith_short_8","alias_value":"MXWMEQHF","created_at":"2026-07-05T08:11:24Z"}],"graph_snapshots":[{"event_id":"sha256:a98936b191ed02222a5ea22f6568ff0b9bab9766b5267fad979719c60ebfbfe6","target":"graph","created_at":"2026-07-05T08:11:24Z","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/2404.10156/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The adoption of Vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles ","authors_text":"Alper Yilmaz, Pouyan Navard, Shehan Perera","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-15T22:12:05Z","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10156","kind":"arxiv","version":2},"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:d17849a0aaf2d08b43cad9434a5edff620bcd93c98576602683e79e33a3c9a15","target":"record","created_at":"2026-07-05T08:11:24Z","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":"4e7112d5c46a10ca7f6189cfc429ee6c60fd1ebfa0469a7059150b58f4e61fbb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-15T22:12:05Z","title_canon_sha256":"7940bdbc55045d4eb75e1aa3bb77c647d3b30a4ab110abae22d88c6b0231ab45"},"schema_version":"1.0","source":{"id":"2404.10156","kind":"arxiv","version":2}},"canonical_sha256":"65ecc240e5526e9192fbc504d320ebb28d26e13d601342756fb04125c69b0875","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"65ecc240e5526e9192fbc504d320ebb28d26e13d601342756fb04125c69b0875","first_computed_at":"2026-07-05T08:11:24.233281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:11:24.233281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ibCru+K5aCi6mRflZ1lVtRIo9YbODUAlOErYD6mDK5ORXXSldU5Eq6gyM8LhgMi2SpzRBEJnvNej2MM47YyLBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:11:24.233764Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.10156","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d17849a0aaf2d08b43cad9434a5edff620bcd93c98576602683e79e33a3c9a15","sha256:a98936b191ed02222a5ea22f6568ff0b9bab9766b5267fad979719c60ebfbfe6"],"state_sha256":"fd61d7c9e7d8ee07de63b45cc5fa20820cafe297125462e72b16fc9cb0dcb9db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vnrssmZBnJFaFe/2WVCgLxhNUWpwmr3XLCb9r46LPC10R47m8DIyDFtaVdASunrJducDEwPjChsa0ql/mgCbDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T21:47:03.480215Z","bundle_sha256":"d92f6fed5d36802d68b2fe8877fbef909378b6898d6f068c325a8ff0350ff55d"}}