{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZOJH5FP6E5DCJRNLKKO6CYS2FA","short_pith_number":"pith:ZOJH5FP6","canonical_record":{"source":{"id":"1812.07816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-19T08:49:50Z","cross_cats_sorted":["cs.CV","cs.PF","stat.ML"],"title_canon_sha256":"b601c74993bc38a660a4a8acbf5bc87e4225f9ff7f8cc58e0a8d0d10e1435e2d","abstract_canon_sha256":"daa27596c49cbeabe4aa1b0742f2f9dc4ed66007f2dc7fefdd1ffc2f75f4e708"},"schema_version":"1.0"},"canonical_sha256":"cb927e95fe274624c5ab529de1625a2819ab068f1ec045e0be12d2a29ddfcc6e","source":{"kind":"arxiv","id":"1812.07816","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07816","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07816v1","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07816","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"ZOJH5FP6E5DC","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZOJH5FP6E5DCJRNL","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZOJH5FP6","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZOJH5FP6E5DCJRNLKKO6CYS2FA","target":"record","payload":{"canonical_record":{"source":{"id":"1812.07816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-19T08:49:50Z","cross_cats_sorted":["cs.CV","cs.PF","stat.ML"],"title_canon_sha256":"b601c74993bc38a660a4a8acbf5bc87e4225f9ff7f8cc58e0a8d0d10e1435e2d","abstract_canon_sha256":"daa27596c49cbeabe4aa1b0742f2f9dc4ed66007f2dc7fefdd1ffc2f75f4e708"},"schema_version":"1.0"},"canonical_sha256":"cb927e95fe274624c5ab529de1625a2819ab068f1ec045e0be12d2a29ddfcc6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:55.889910Z","signature_b64":"60BUDThIQCoJopJICNLszXncYUWYxQhRqiekvP7JnrmxBbsvWSiMjbl4vhw6vCfJ7rNZuIpGwkE7/QnBDlPMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb927e95fe274624c5ab529de1625a2819ab068f1ec045e0be12d2a29ddfcc6e","last_reissued_at":"2026-05-17T23:57:55.889350Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:55.889350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.07816","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":"2026-05-17T23:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C71MiPrhx8ykIhAHlp0aNJeWqYAQpzacFeSmwzFfUJ/f2qvbXm9G9L1XrztKhfdszvZrXcsVq3HZqLLSxlyeDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T10:05:09.645627Z"},"content_sha256":"76e100c7d42c318a56e67bedae813b112b4d73065de74734dba7fbb09abb65dd","schema_version":"1.0","event_id":"sha256:76e100c7d42c318a56e67bedae813b112b4d73065de74734dba7fbb09abb65dd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZOJH5FP6E5DCJRNLKKO6CYS2FA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast and Accurate 3D Medical Image Segmentation with Data-swapping Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.PF","stat.ML"],"primary_cat":"cs.LG","authors_text":"Haruki Imai, Kiyokuni Kawachiya, Samuel Matzek, Tung D. Le, Yasushi Negishi","submitted_at":"2018-12-19T08:49:50Z","abstract_excerpt":"Deep neural network models used for medical image segmentation are large because they are trained with high-resolution three-dimensional (3D) images. Graphics processing units (GPUs) are widely used to accelerate the trainings. However, the memory on a GPU is not large enough to train the models. A popular approach to tackling this problem is patch-based method, which divides a large image into small patches and trains the models with these small patches. However, this method would degrade the segmentation quality if a target object spans multiple patches. In this paper, we propose a novel app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07816","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":""},"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":"2026-05-17T23:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t/98pT82sPCh58dqMMZbQrIByDV2M4EXUv5wS+tqBD8s36i5XSUOhZ0TslznVGK3X/WsTaqBB2FfUpxhWUa6Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T10:05:09.645983Z"},"content_sha256":"9503c6df200efc06329016720df4a17f0baeba712bf97064ceedc626fa609113","schema_version":"1.0","event_id":"sha256:9503c6df200efc06329016720df4a17f0baeba712bf97064ceedc626fa609113"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA/bundle.json","state_url":"https://pith.science/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA/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":"2026-05-28T10:05:09Z","links":{"resolver":"https://pith.science/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA","bundle":"https://pith.science/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA/bundle.json","state":"https://pith.science/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZOJH5FP6E5DCJRNLKKO6CYS2FA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZOJH5FP6E5DCJRNLKKO6CYS2FA","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":"daa27596c49cbeabe4aa1b0742f2f9dc4ed66007f2dc7fefdd1ffc2f75f4e708","cross_cats_sorted":["cs.CV","cs.PF","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-19T08:49:50Z","title_canon_sha256":"b601c74993bc38a660a4a8acbf5bc87e4225f9ff7f8cc58e0a8d0d10e1435e2d"},"schema_version":"1.0","source":{"id":"1812.07816","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07816","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07816v1","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07816","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"ZOJH5FP6E5DC","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZOJH5FP6E5DCJRNL","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZOJH5FP6","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:9503c6df200efc06329016720df4a17f0baeba712bf97064ceedc626fa609113","target":"graph","created_at":"2026-05-17T23:57:55Z","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"},"paper":{"abstract_excerpt":"Deep neural network models used for medical image segmentation are large because they are trained with high-resolution three-dimensional (3D) images. Graphics processing units (GPUs) are widely used to accelerate the trainings. However, the memory on a GPU is not large enough to train the models. A popular approach to tackling this problem is patch-based method, which divides a large image into small patches and trains the models with these small patches. However, this method would degrade the segmentation quality if a target object spans multiple patches. In this paper, we propose a novel app","authors_text":"Haruki Imai, Kiyokuni Kawachiya, Samuel Matzek, Tung D. Le, Yasushi Negishi","cross_cats":["cs.CV","cs.PF","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-19T08:49:50Z","title":"Fast and Accurate 3D Medical Image Segmentation with Data-swapping Method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07816","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":"sha256:76e100c7d42c318a56e67bedae813b112b4d73065de74734dba7fbb09abb65dd","target":"record","created_at":"2026-05-17T23:57:55Z","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":"daa27596c49cbeabe4aa1b0742f2f9dc4ed66007f2dc7fefdd1ffc2f75f4e708","cross_cats_sorted":["cs.CV","cs.PF","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-19T08:49:50Z","title_canon_sha256":"b601c74993bc38a660a4a8acbf5bc87e4225f9ff7f8cc58e0a8d0d10e1435e2d"},"schema_version":"1.0","source":{"id":"1812.07816","kind":"arxiv","version":1}},"canonical_sha256":"cb927e95fe274624c5ab529de1625a2819ab068f1ec045e0be12d2a29ddfcc6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cb927e95fe274624c5ab529de1625a2819ab068f1ec045e0be12d2a29ddfcc6e","first_computed_at":"2026-05-17T23:57:55.889350Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:55.889350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"60BUDThIQCoJopJICNLszXncYUWYxQhRqiekvP7JnrmxBbsvWSiMjbl4vhw6vCfJ7rNZuIpGwkE7/QnBDlPMBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:55.889910Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.07816","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76e100c7d42c318a56e67bedae813b112b4d73065de74734dba7fbb09abb65dd","sha256:9503c6df200efc06329016720df4a17f0baeba712bf97064ceedc626fa609113"],"state_sha256":"84014e1a3717927aa46e34b546970cee44e036614b5139ea7e8962e3b0bf7b6e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/DTwoUWjWUOkb+Fd7p+xWycevTjN/sOOwnZg5dT39KJV3FNmnFUVTloS5pHfRwPb62NHYKf7WA8Go8YiRLW2Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T10:05:09.648018Z","bundle_sha256":"494aad8b548a76084b7823c6ff77d236f0b409d132cd9fc0b6269c060367db40"}}