{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:BVFST4GFORBOFSMZTHKPTSVHQV","short_pith_number":"pith:BVFST4GF","canonical_record":{"source":{"id":"2207.08518","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-18T11:30:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0bc385d1e92c3ac720d6dd352a80c52108c8520f36a2115edbe3ea46bf63463a","abstract_canon_sha256":"875c58b2f911cb998a30962894eb30416b35ec4205845961ff3b0082636992bc"},"schema_version":"1.0"},"canonical_sha256":"0d4b29f0c57442e2c99999d4f9caa78564f720acd3da2bf7b1d5c2d69055fcc9","source":{"kind":"arxiv","id":"2207.08518","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.08518","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"arxiv_version","alias_value":"2207.08518v2","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.08518","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"pith_short_12","alias_value":"BVFST4GFORBO","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"BVFST4GFORBOFSMZ","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"BVFST4GF","created_at":"2026-07-05T05:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:BVFST4GFORBOFSMZTHKPTSVHQV","target":"record","payload":{"canonical_record":{"source":{"id":"2207.08518","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-18T11:30:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0bc385d1e92c3ac720d6dd352a80c52108c8520f36a2115edbe3ea46bf63463a","abstract_canon_sha256":"875c58b2f911cb998a30962894eb30416b35ec4205845961ff3b0082636992bc"},"schema_version":"1.0"},"canonical_sha256":"0d4b29f0c57442e2c99999d4f9caa78564f720acd3da2bf7b1d5c2d69055fcc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:31:15.938425Z","signature_b64":"Z5aotimHyWhPvFVa+VdYo66Iv4CNJzn8J+4UA5iO/DJaQyjYIU2QmTC/pRzYdygntKFPVqEYnm5o6gMKbRJoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d4b29f0c57442e2c99999d4f9caa78564f720acd3da2bf7b1d5c2d69055fcc9","last_reissued_at":"2026-07-05T05:31:15.937928Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:31:15.937928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.08518","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":"2026-07-05T05:31:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Ym3XAgq5kp2MHscK0VQ0qWHiWmSXZKuelClRtOBK9CtZ5xD+jA8dppLCcxg2Frv0br3TlT/ip+lu7H1R+RIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:18:31.847636Z"},"content_sha256":"3d17815ca81e8d0b97576f5cc25f89507237a6a500f158158118d4f6a530aa58","schema_version":"1.0","event_id":"sha256:3d17815ca81e8d0b97576f5cc25f89507237a6a500f158158118d4f6a530aa58"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:BVFST4GFORBOFSMZTHKPTSVHQV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Amirhossein Kazerouni, Dorit Merhof, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, Milad Soltany, Moein Heidari, Reza Azad","submitted_at":"2022-07-18T11:30:06Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.08518","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/2207.08518/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":"2026-07-05T05:31:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n4Bn7Uz8AlsWdQ9HfgrLT19p4dlWKjetijXqTL/CrOuyBJHiG/U0xj7LXLtKlfDaDTmHw3tAgGi7gwP3H1wvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:18:31.848005Z"},"content_sha256":"0ee95246e21e3c93a5ae9bb0839a8d6d2c0a6e228106cca5f9c31ff869b41932","schema_version":"1.0","event_id":"sha256:0ee95246e21e3c93a5ae9bb0839a8d6d2c0a6e228106cca5f9c31ff869b41932"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BVFST4GFORBOFSMZTHKPTSVHQV/bundle.json","state_url":"https://pith.science/pith/BVFST4GFORBOFSMZTHKPTSVHQV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BVFST4GFORBOFSMZTHKPTSVHQV/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":"2026-07-07T13:18:31Z","links":{"resolver":"https://pith.science/pith/BVFST4GFORBOFSMZTHKPTSVHQV","bundle":"https://pith.science/pith/BVFST4GFORBOFSMZTHKPTSVHQV/bundle.json","state":"https://pith.science/pith/BVFST4GFORBOFSMZTHKPTSVHQV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BVFST4GFORBOFSMZTHKPTSVHQV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:BVFST4GFORBOFSMZTHKPTSVHQV","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":"875c58b2f911cb998a30962894eb30416b35ec4205845961ff3b0082636992bc","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-18T11:30:06Z","title_canon_sha256":"0bc385d1e92c3ac720d6dd352a80c52108c8520f36a2115edbe3ea46bf63463a"},"schema_version":"1.0","source":{"id":"2207.08518","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.08518","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"arxiv_version","alias_value":"2207.08518v2","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.08518","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"pith_short_12","alias_value":"BVFST4GFORBO","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"BVFST4GFORBOFSMZ","created_at":"2026-07-05T05:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"BVFST4GF","created_at":"2026-07-05T05:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:0ee95246e21e3c93a5ae9bb0839a8d6d2c0a6e228106cca5f9c31ff869b41932","target":"graph","created_at":"2026-07-05T05:31:15Z","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/2207.08518/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer fo","authors_text":"Amirhossein Kazerouni, Dorit Merhof, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, Milad Soltany, Moein Heidari, Reza Azad","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-18T11:30:06Z","title":"HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.08518","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":"sha256:3d17815ca81e8d0b97576f5cc25f89507237a6a500f158158118d4f6a530aa58","target":"record","created_at":"2026-07-05T05:31:15Z","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":"875c58b2f911cb998a30962894eb30416b35ec4205845961ff3b0082636992bc","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-18T11:30:06Z","title_canon_sha256":"0bc385d1e92c3ac720d6dd352a80c52108c8520f36a2115edbe3ea46bf63463a"},"schema_version":"1.0","source":{"id":"2207.08518","kind":"arxiv","version":2}},"canonical_sha256":"0d4b29f0c57442e2c99999d4f9caa78564f720acd3da2bf7b1d5c2d69055fcc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d4b29f0c57442e2c99999d4f9caa78564f720acd3da2bf7b1d5c2d69055fcc9","first_computed_at":"2026-07-05T05:31:15.937928Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:31:15.937928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z5aotimHyWhPvFVa+VdYo66Iv4CNJzn8J+4UA5iO/DJaQyjYIU2QmTC/pRzYdygntKFPVqEYnm5o6gMKbRJoBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:31:15.938425Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.08518","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d17815ca81e8d0b97576f5cc25f89507237a6a500f158158118d4f6a530aa58","sha256:0ee95246e21e3c93a5ae9bb0839a8d6d2c0a6e228106cca5f9c31ff869b41932"],"state_sha256":"ccff113c6840cf612f29f22e57e5ffbcb66b5c34ccd836ad6a934106159b5cfa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wC8Yl6wxpJdQ5yb5N6qCdi6sDHFm7PyWWQtSpen+NBu8hcOb/a1jF6z++3Z5dSv9jqJBqPBjBCdc5+3kVaq6Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:18:31.850217Z","bundle_sha256":"e83dc5e1ded969eeb244fa864a6a495c6bffeeda0feddc162dafdd7604a94bc8"}}