{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:4KE5KBEBXPLHELV35JCQIETOCT","short_pith_number":"pith:4KE5KBEB","canonical_record":{"source":{"id":"2210.15949","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-28T07:12:15Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"777bbcade1da0f71c827641fec3c3439490a2afc425b417be9c623ba3c952cf8","abstract_canon_sha256":"dd4bb5ff33837e7ad227022f3fb63f83e0af6c84d3b9138f05a1b6097dcef5d6"},"schema_version":"1.0"},"canonical_sha256":"e289d50481bbd6722ebbea4504126e14f11ffedfbfb9994041b649454d85e17c","source":{"kind":"arxiv","id":"2210.15949","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.15949","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"arxiv_version","alias_value":"2210.15949v1","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15949","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"pith_short_12","alias_value":"4KE5KBEBXPLH","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"pith_short_16","alias_value":"4KE5KBEBXPLHELV3","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"pith_short_8","alias_value":"4KE5KBEB","created_at":"2026-07-05T05:11:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:4KE5KBEBXPLHELV35JCQIETOCT","target":"record","payload":{"canonical_record":{"source":{"id":"2210.15949","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-28T07:12:15Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"777bbcade1da0f71c827641fec3c3439490a2afc425b417be9c623ba3c952cf8","abstract_canon_sha256":"dd4bb5ff33837e7ad227022f3fb63f83e0af6c84d3b9138f05a1b6097dcef5d6"},"schema_version":"1.0"},"canonical_sha256":"e289d50481bbd6722ebbea4504126e14f11ffedfbfb9994041b649454d85e17c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:28.409556Z","signature_b64":"nNuDYaBB2IrF3FFMzytk2k5l0zpQEON7QXOCNl5GwRt6WgyqQRZ/gCA3Mf878JMWP1F/BsI6G5M0sdfC4VZxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e289d50481bbd6722ebbea4504126e14f11ffedfbfb9994041b649454d85e17c","last_reissued_at":"2026-07-05T05:11:28.408954Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:28.408954Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.15949","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":"2026-07-05T05:11:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"28bZyYW9rCCPf2gDRa4pnZQTzMbTGScI2jjnLHBPHRNt/gdlzql8dO4P82BaGuNkAEv1uv1bLaT5qCqn4hiiDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:21:02.819004Z"},"content_sha256":"b0b513474b096b9990a82ebc6a6d604bd2407aa77bd1bb217411e67931d10fa3","schema_version":"1.0","event_id":"sha256:b0b513474b096b9990a82ebc6a6d604bd2407aa77bd1bb217411e67931d10fa3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:4KE5KBEBXPLHELV35JCQIETOCT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Anca-Ligia Grosu, Constantinos Zamboglou, Dejan Kostyszyn, Radu Grosu, Shrajan Bhandary, Tobias Fechter, Zahra Babaiee","submitted_at":"2022-10-28T07:12:15Z","abstract_excerpt":"Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, access to large-scale datasets with annotated regions of interest is scarce, and obtaining good results is thus difficult. To overcome these challenges, we introduce IB-U-Nets, a novel architecture with inductive bias, inspired by the visual processing in vertebrates. With the 3D U-Net as the base, we add two 3D residual components to the second enc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15949","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.15949/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":"2026-07-05T05:11:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HI94mHh0oYejgReb6RTb+RfbSQgkIC6t/OFagOBnpCc5Gk3HuOuFmmsYpC7Wa7Jf/r21G3YHVd7oJ6rC16gKBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:21:02.819516Z"},"content_sha256":"6fe25eaa7b2b56cc1ec7e44e8b310f7bc6522f159d34e2b9543925e2d72d56e3","schema_version":"1.0","event_id":"sha256:6fe25eaa7b2b56cc1ec7e44e8b310f7bc6522f159d34e2b9543925e2d72d56e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4KE5KBEBXPLHELV35JCQIETOCT/bundle.json","state_url":"https://pith.science/pith/4KE5KBEBXPLHELV35JCQIETOCT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4KE5KBEBXPLHELV35JCQIETOCT/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":"2026-08-03T19:21:02Z","links":{"resolver":"https://pith.science/pith/4KE5KBEBXPLHELV35JCQIETOCT","bundle":"https://pith.science/pith/4KE5KBEBXPLHELV35JCQIETOCT/bundle.json","state":"https://pith.science/pith/4KE5KBEBXPLHELV35JCQIETOCT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4KE5KBEBXPLHELV35JCQIETOCT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:4KE5KBEBXPLHELV35JCQIETOCT","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":"dd4bb5ff33837e7ad227022f3fb63f83e0af6c84d3b9138f05a1b6097dcef5d6","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-28T07:12:15Z","title_canon_sha256":"777bbcade1da0f71c827641fec3c3439490a2afc425b417be9c623ba3c952cf8"},"schema_version":"1.0","source":{"id":"2210.15949","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.15949","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"arxiv_version","alias_value":"2210.15949v1","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15949","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"pith_short_12","alias_value":"4KE5KBEBXPLH","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"pith_short_16","alias_value":"4KE5KBEBXPLHELV3","created_at":"2026-07-05T05:11:28Z"},{"alias_kind":"pith_short_8","alias_value":"4KE5KBEB","created_at":"2026-07-05T05:11:28Z"}],"graph_snapshots":[{"event_id":"sha256:6fe25eaa7b2b56cc1ec7e44e8b310f7bc6522f159d34e2b9543925e2d72d56e3","target":"graph","created_at":"2026-07-05T05:11:28Z","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/2210.15949/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, access to large-scale datasets with annotated regions of interest is scarce, and obtaining good results is thus difficult. To overcome these challenges, we introduce IB-U-Nets, a novel architecture with inductive bias, inspired by the visual processing in vertebrates. With the 3D U-Net as the base, we add two 3D residual components to the second enc","authors_text":"Anca-Ligia Grosu, Constantinos Zamboglou, Dejan Kostyszyn, Radu Grosu, Shrajan Bhandary, Tobias Fechter, Zahra Babaiee","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-28T07:12:15Z","title":"IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15949","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:b0b513474b096b9990a82ebc6a6d604bd2407aa77bd1bb217411e67931d10fa3","target":"record","created_at":"2026-07-05T05:11:28Z","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":"dd4bb5ff33837e7ad227022f3fb63f83e0af6c84d3b9138f05a1b6097dcef5d6","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-28T07:12:15Z","title_canon_sha256":"777bbcade1da0f71c827641fec3c3439490a2afc425b417be9c623ba3c952cf8"},"schema_version":"1.0","source":{"id":"2210.15949","kind":"arxiv","version":1}},"canonical_sha256":"e289d50481bbd6722ebbea4504126e14f11ffedfbfb9994041b649454d85e17c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e289d50481bbd6722ebbea4504126e14f11ffedfbfb9994041b649454d85e17c","first_computed_at":"2026-07-05T05:11:28.408954Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:11:28.408954Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nNuDYaBB2IrF3FFMzytk2k5l0zpQEON7QXOCNl5GwRt6WgyqQRZ/gCA3Mf878JMWP1F/BsI6G5M0sdfC4VZxCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:11:28.409556Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.15949","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0b513474b096b9990a82ebc6a6d604bd2407aa77bd1bb217411e67931d10fa3","sha256:6fe25eaa7b2b56cc1ec7e44e8b310f7bc6522f159d34e2b9543925e2d72d56e3"],"state_sha256":"1c46e0554e11db097e6519db8ae92583371bd60d31d608274b581b3035a4bcbf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aYhQX+iPiPdlt6ITJCQxnkylbjTdlRtq02kTsD16X47l/+9uz301T5YWKmDRYfRJ+C4Z+MuzVe1FZNo2neqMDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:21:02.823965Z","bundle_sha256":"bdfded123b7791470b115162edf2d3b13518c9062e64c28d97d05398f80d21b1"}}