{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BEEB4ZUVN3UQLHUI27LU5IKNWP","short_pith_number":"pith:BEEB4ZUV","canonical_record":{"source":{"id":"2410.15496","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-20T20:26:09Z","cross_cats_sorted":[],"title_canon_sha256":"a082f2232e6d04fc88066f481b004fb308e1ca097901f233a14dabb772e83535","abstract_canon_sha256":"e7fa9db10d7e74e8c4e266abe01195f81647b427af0adc8df772ff9e7fbb4865"},"schema_version":"1.0"},"canonical_sha256":"09081e66956ee9059e88d7d74ea14db3f8300dc08f976549db25be462269d878","source":{"kind":"arxiv","id":"2410.15496","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15496","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15496v1","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15496","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_12","alias_value":"BEEB4ZUVN3UQ","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_16","alias_value":"BEEB4ZUVN3UQLHUI","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_8","alias_value":"BEEB4ZUV","created_at":"2026-07-05T09:23:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BEEB4ZUVN3UQLHUI27LU5IKNWP","target":"record","payload":{"canonical_record":{"source":{"id":"2410.15496","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-20T20:26:09Z","cross_cats_sorted":[],"title_canon_sha256":"a082f2232e6d04fc88066f481b004fb308e1ca097901f233a14dabb772e83535","abstract_canon_sha256":"e7fa9db10d7e74e8c4e266abe01195f81647b427af0adc8df772ff9e7fbb4865"},"schema_version":"1.0"},"canonical_sha256":"09081e66956ee9059e88d7d74ea14db3f8300dc08f976549db25be462269d878","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:20.409553Z","signature_b64":"aPF2feLRFbwJFamn8UMgLgJmTqNO25ghitytDBd5f4lZ73w38iJ1EYUcNZund/edaTUP5OfOvafJiqIbj/lZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09081e66956ee9059e88d7d74ea14db3f8300dc08f976549db25be462269d878","last_reissued_at":"2026-07-05T09:23:20.409139Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:20.409139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.15496","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":"2026-07-05T09:23:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NyhAyONOBgX9DvIil0UboFidNxmuOc1vZmCHN6w9Q+8hmXq/gjdMwejmuKz+y/cUo1k+Xn6uX3ONfUrXYSOXCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:34:27.625777Z"},"content_sha256":"b749f8bcbe72aea4b3ead85c2d60278f31562f37ae50645db3b56f92e2330f45","schema_version":"1.0","event_id":"sha256:b749f8bcbe72aea4b3ead85c2d60278f31562f37ae50645db3b56f92e2330f45"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BEEB4ZUVN3UQLHUI27LU5IKNWP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Taming Mambas for Voxel Level 3D Medical Image Segmentation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Costantino Grana, Elisa Ficarra, Federico Bolelli, Kevin Marchesini, Luca Lumetti, Vittorio Pipoli","submitted_at":"2024-10-20T20:26:09Z","abstract_excerpt":"Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with their distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas transformers are hindered by their substantial memory requirements as well as they data hungriness, making them not ideal for processing 3D medical volumes at a fine-grained level. For these reasons, fully convolutional neural networks, as nnUNet, still dominate the scene when segmenting medical structures in 3D "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15496","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/2410.15496/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":"2026-07-05T09:23:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+tEBgGbPNAdIjSTMuaioxzMMiVxGNDjHhgpdEESCMY7Q3Gfe4go7eCUEfekOwlJGmJPbSt7aEfT1IU4pOwP5Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:34:27.626160Z"},"content_sha256":"89565ca19a708a9bf53e717492138e13e1a65d37b08d3fd2f632eedb9e486c7a","schema_version":"1.0","event_id":"sha256:89565ca19a708a9bf53e717492138e13e1a65d37b08d3fd2f632eedb9e486c7a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP/bundle.json","state_url":"https://pith.science/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP/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":"2026-07-07T04:34:27Z","links":{"resolver":"https://pith.science/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP","bundle":"https://pith.science/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP/bundle.json","state":"https://pith.science/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BEEB4ZUVN3UQLHUI27LU5IKNWP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BEEB4ZUVN3UQLHUI27LU5IKNWP","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":"e7fa9db10d7e74e8c4e266abe01195f81647b427af0adc8df772ff9e7fbb4865","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-20T20:26:09Z","title_canon_sha256":"a082f2232e6d04fc88066f481b004fb308e1ca097901f233a14dabb772e83535"},"schema_version":"1.0","source":{"id":"2410.15496","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15496","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15496v1","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15496","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_12","alias_value":"BEEB4ZUVN3UQ","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_16","alias_value":"BEEB4ZUVN3UQLHUI","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_8","alias_value":"BEEB4ZUV","created_at":"2026-07-05T09:23:20Z"}],"graph_snapshots":[{"event_id":"sha256:89565ca19a708a9bf53e717492138e13e1a65d37b08d3fd2f632eedb9e486c7a","target":"graph","created_at":"2026-07-05T09:23:20Z","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/2410.15496/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with their distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas transformers are hindered by their substantial memory requirements as well as they data hungriness, making them not ideal for processing 3D medical volumes at a fine-grained level. For these reasons, fully convolutional neural networks, as nnUNet, still dominate the scene when segmenting medical structures in 3D ","authors_text":"Costantino Grana, Elisa Ficarra, Federico Bolelli, Kevin Marchesini, Luca Lumetti, Vittorio Pipoli","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-20T20:26:09Z","title":"Taming Mambas for Voxel Level 3D Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15496","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:b749f8bcbe72aea4b3ead85c2d60278f31562f37ae50645db3b56f92e2330f45","target":"record","created_at":"2026-07-05T09:23:20Z","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":"e7fa9db10d7e74e8c4e266abe01195f81647b427af0adc8df772ff9e7fbb4865","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-20T20:26:09Z","title_canon_sha256":"a082f2232e6d04fc88066f481b004fb308e1ca097901f233a14dabb772e83535"},"schema_version":"1.0","source":{"id":"2410.15496","kind":"arxiv","version":1}},"canonical_sha256":"09081e66956ee9059e88d7d74ea14db3f8300dc08f976549db25be462269d878","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09081e66956ee9059e88d7d74ea14db3f8300dc08f976549db25be462269d878","first_computed_at":"2026-07-05T09:23:20.409139Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:23:20.409139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aPF2feLRFbwJFamn8UMgLgJmTqNO25ghitytDBd5f4lZ73w38iJ1EYUcNZund/edaTUP5OfOvafJiqIbj/lZBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:23:20.409553Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.15496","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b749f8bcbe72aea4b3ead85c2d60278f31562f37ae50645db3b56f92e2330f45","sha256:89565ca19a708a9bf53e717492138e13e1a65d37b08d3fd2f632eedb9e486c7a"],"state_sha256":"8f62b57024d95ace5720b6fa041225920cad7a99eb519a5c5b7f64ce31c08a72"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sw4X8qsm3r/KVhkQU/6aChEkPeSLzEOuX/IPrSZ9nY3FtzuQyhoPst/WUxk+8mexyMkGAt3oHZFyqzC0Wk87AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T04:34:27.628054Z","bundle_sha256":"5764d957b0a59aa60ff94ea3539efb2f0e455ca1fe5f6f7bc5fc705bdfe39460"}}