{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:YXW2GEXY5N4KYTSSN373J7IYVS","short_pith_number":"pith:YXW2GEXY","canonical_record":{"source":{"id":"2102.13645","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-26T18:49:13Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"97257041f9c21e6e1c145f7b54e4eb0c7302e093de406f81fbda2feab2578add","abstract_canon_sha256":"422461748eb6b4b29ef87fe5db38f091e80e2ba509463c9e4e1ad4c9b93af110"},"schema_version":"1.0"},"canonical_sha256":"c5eda312f8eb78ac4e526effb4fd18ac9c5f350088b506e093d368f86d54eab7","source":{"kind":"arxiv","id":"2102.13645","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.13645","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"arxiv_version","alias_value":"2102.13645v2","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.13645","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"pith_short_12","alias_value":"YXW2GEXY5N4K","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"pith_short_16","alias_value":"YXW2GEXY5N4KYTSS","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"pith_short_8","alias_value":"YXW2GEXY","created_at":"2026-07-05T04:11:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:YXW2GEXY5N4KYTSSN373J7IYVS","target":"record","payload":{"canonical_record":{"source":{"id":"2102.13645","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-26T18:49:13Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"97257041f9c21e6e1c145f7b54e4eb0c7302e093de406f81fbda2feab2578add","abstract_canon_sha256":"422461748eb6b4b29ef87fe5db38f091e80e2ba509463c9e4e1ad4c9b93af110"},"schema_version":"1.0"},"canonical_sha256":"c5eda312f8eb78ac4e526effb4fd18ac9c5f350088b506e093d368f86d54eab7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:11:05.056783Z","signature_b64":"Pn3bQUt7H7FrM00OiERMvE1ox1Ez19ej9cEB8xlvlav+9bN6roGHfdhImHHQjopGOKSbxuzYxbsJRn0VhkoGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5eda312f8eb78ac4e526effb4fd18ac9c5f350088b506e093d368f86d54eab7","last_reissued_at":"2026-07-05T04:11:05.056431Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:11:05.056431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.13645","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":"2026-07-05T04:11:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BxxlI90HJv7Hc086z3213ND236ADlXDbihpCXqEaGUPm93H01mN06NRs60OuHt5bRjTAKJv0os5Sg92J2paYDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:15:29.622507Z"},"content_sha256":"5b151ee4f9d7b5c5c8b715dba90b78c537949ed4128f3ec2a24d9b2610c50456","schema_version":"1.0","event_id":"sha256:5b151ee4f9d7b5c5c8b715dba90b78c537949ed4128f3ec2a24d9b2610c50456"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:YXW2GEXY5N4KYTSSN373J7IYVS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolution-Free Medical Image Segmentation using Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Ali Gholipour, Davood Karimi, Serge Vasylechko","submitted_at":"2021-02-26T18:49:13Z","abstract_excerpt":"Like other applications in computer vision, medical image segmentation has been most successfully addressed using deep learning models that rely on the convolution operation as their main building block. Convolutions enjoy important properties such as sparse interactions, weight sharing, and translation equivariance. These properties give convolutional neural networks (CNNs) a strong and useful inductive bias for vision tasks. In this work we show that a different method, based entirely on self-attention between neighboring image patches and without any convolution operations, can achieve comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.13645","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/2102.13645/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":"2026-07-05T04:11:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B9zRzexr98/G3jyiP2KxxsVw/ioS92AnmfkFOcExTvq1Sg/5BapW6gdlLDVRpCrqDML4nxF1pmy2MQzLHMOtDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:15:29.623356Z"},"content_sha256":"08f99786a03111bdc6515f50d55098643bb87dad538a6287de0c67b4d0d3dc2d","schema_version":"1.0","event_id":"sha256:08f99786a03111bdc6515f50d55098643bb87dad538a6287de0c67b4d0d3dc2d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YXW2GEXY5N4KYTSSN373J7IYVS/bundle.json","state_url":"https://pith.science/pith/YXW2GEXY5N4KYTSSN373J7IYVS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YXW2GEXY5N4KYTSSN373J7IYVS/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":"2026-08-05T00:15:29Z","links":{"resolver":"https://pith.science/pith/YXW2GEXY5N4KYTSSN373J7IYVS","bundle":"https://pith.science/pith/YXW2GEXY5N4KYTSSN373J7IYVS/bundle.json","state":"https://pith.science/pith/YXW2GEXY5N4KYTSSN373J7IYVS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YXW2GEXY5N4KYTSSN373J7IYVS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YXW2GEXY5N4KYTSSN373J7IYVS","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":"422461748eb6b4b29ef87fe5db38f091e80e2ba509463c9e4e1ad4c9b93af110","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-26T18:49:13Z","title_canon_sha256":"97257041f9c21e6e1c145f7b54e4eb0c7302e093de406f81fbda2feab2578add"},"schema_version":"1.0","source":{"id":"2102.13645","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.13645","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"arxiv_version","alias_value":"2102.13645v2","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.13645","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"pith_short_12","alias_value":"YXW2GEXY5N4K","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"pith_short_16","alias_value":"YXW2GEXY5N4KYTSS","created_at":"2026-07-05T04:11:05Z"},{"alias_kind":"pith_short_8","alias_value":"YXW2GEXY","created_at":"2026-07-05T04:11:05Z"}],"graph_snapshots":[{"event_id":"sha256:08f99786a03111bdc6515f50d55098643bb87dad538a6287de0c67b4d0d3dc2d","target":"graph","created_at":"2026-07-05T04:11:05Z","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/2102.13645/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Like other applications in computer vision, medical image segmentation has been most successfully addressed using deep learning models that rely on the convolution operation as their main building block. Convolutions enjoy important properties such as sparse interactions, weight sharing, and translation equivariance. These properties give convolutional neural networks (CNNs) a strong and useful inductive bias for vision tasks. In this work we show that a different method, based entirely on self-attention between neighboring image patches and without any convolution operations, can achieve comp","authors_text":"Ali Gholipour, Davood Karimi, Serge Vasylechko","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-26T18:49:13Z","title":"Convolution-Free Medical Image Segmentation using Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.13645","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":"sha256:5b151ee4f9d7b5c5c8b715dba90b78c537949ed4128f3ec2a24d9b2610c50456","target":"record","created_at":"2026-07-05T04:11:05Z","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":"422461748eb6b4b29ef87fe5db38f091e80e2ba509463c9e4e1ad4c9b93af110","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-02-26T18:49:13Z","title_canon_sha256":"97257041f9c21e6e1c145f7b54e4eb0c7302e093de406f81fbda2feab2578add"},"schema_version":"1.0","source":{"id":"2102.13645","kind":"arxiv","version":2}},"canonical_sha256":"c5eda312f8eb78ac4e526effb4fd18ac9c5f350088b506e093d368f86d54eab7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c5eda312f8eb78ac4e526effb4fd18ac9c5f350088b506e093d368f86d54eab7","first_computed_at":"2026-07-05T04:11:05.056431Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:11:05.056431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pn3bQUt7H7FrM00OiERMvE1ox1Ez19ej9cEB8xlvlav+9bN6roGHfdhImHHQjopGOKSbxuzYxbsJRn0VhkoGAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:11:05.056783Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.13645","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b151ee4f9d7b5c5c8b715dba90b78c537949ed4128f3ec2a24d9b2610c50456","sha256:08f99786a03111bdc6515f50d55098643bb87dad538a6287de0c67b4d0d3dc2d"],"state_sha256":"e8550cfa9cbc367f5e53989fd2e3f7d353ecbaf38275277ab4bf4d650be5ae16"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l8Rj4TOnS+Ok2AhmbENizOHJZJRx6lsj3fKOG0f2MifVf6DUiPlMzDDXn68hZlW90jKvGFCw8YGR8ulQFQamAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T00:15:29.640999Z","bundle_sha256":"47e855f499f3f7f67451dacc93bd8a5684ddd342fbed01e5dafae0476aa893c7"}}