{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:3MPJKXBSZD3B5R5JQMEANR4BX4","short_pith_number":"pith:3MPJKXBS","canonical_record":{"source":{"id":"2411.02372","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T18:40:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cbcc7c56a26e72b395ce556b8e56d81e2354759de0d567ed90816556eb9f212d","abstract_canon_sha256":"d973aa386254b3d9558f18488da123f9ce6df2f1271a1f5be13752eaaaf8f82d"},"schema_version":"1.0"},"canonical_sha256":"db1e955c32c8f61ec7a9830806c781bf3a970b5825516d858eb452e863414bc3","source":{"kind":"arxiv","id":"2411.02372","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02372","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02372v2","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02372","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"pith_short_12","alias_value":"3MPJKXBSZD3B","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"pith_short_16","alias_value":"3MPJKXBSZD3B5R5J","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"pith_short_8","alias_value":"3MPJKXBS","created_at":"2026-07-05T10:22:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:3MPJKXBSZD3B5R5JQMEANR4BX4","target":"record","payload":{"canonical_record":{"source":{"id":"2411.02372","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T18:40:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cbcc7c56a26e72b395ce556b8e56d81e2354759de0d567ed90816556eb9f212d","abstract_canon_sha256":"d973aa386254b3d9558f18488da123f9ce6df2f1271a1f5be13752eaaaf8f82d"},"schema_version":"1.0"},"canonical_sha256":"db1e955c32c8f61ec7a9830806c781bf3a970b5825516d858eb452e863414bc3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:09.376645Z","signature_b64":"KRzYDTevqS9hwmJzdmh6WSdB+sbD7S17g555fsNQ/Z5ANpSuGBC2U/Rp3Nr1Lujo6x+PlQcINOekcAArSs2zBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db1e955c32c8f61ec7a9830806c781bf3a970b5825516d858eb452e863414bc3","last_reissued_at":"2026-07-05T10:22:09.376051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:09.376051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.02372","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":"2026-07-05T10:22:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OFzbXPO5BFp8cyTncdKQYHz7nWVXP7zg0dhFgprByQMUO6KMj91CYLTGQRDjyx9WTj8Ujj4i+ywW7CEVq7FVAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:43:50.960502Z"},"content_sha256":"ee0e94ceded06acb2a39dc2ccae80165b5da7e73cb5f4878c8fc1a08522640c3","schema_version":"1.0","event_id":"sha256:ee0e94ceded06acb2a39dc2ccae80165b5da7e73cb5f4878c8fc1a08522640c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:3MPJKXBSZD3B5R5JQMEANR4BX4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adrian V. Dalca, Benjamin Billot, Clinton J. Wang, Hallee E. Wong, Mengwei Ren, Neel Dey, P. Ellen Grant, Polina Golland","submitted_at":"2024-11-04T18:40:46Z","abstract_excerpt":"Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a representation learning method that instead anticipates strong domain shifts at training time itself. We first propose a data engine that synthesizes highly variable training samples that would enable generalization to new biomedical contexts. To then train a single 3D network for any voxel-level task, we develop a contrastive learning method that pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02372","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/2411.02372/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":"2026-07-05T10:22:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EmWFIlBr4aXia0GP7uIYb/zh+wpqP46enG7yFmzA9sU471cHi7P87sSAea9Qsk4QLOooUccKSzSM2BDZ0owwAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:43:50.961392Z"},"content_sha256":"48cfa674d150e601b759589ca4a89f5acb9a68271b77cd6f99daaefb25f326e4","schema_version":"1.0","event_id":"sha256:48cfa674d150e601b759589ca4a89f5acb9a68271b77cd6f99daaefb25f326e4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3MPJKXBSZD3B5R5JQMEANR4BX4/bundle.json","state_url":"https://pith.science/pith/3MPJKXBSZD3B5R5JQMEANR4BX4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3MPJKXBSZD3B5R5JQMEANR4BX4/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":"2026-08-10T18:43:50Z","links":{"resolver":"https://pith.science/pith/3MPJKXBSZD3B5R5JQMEANR4BX4","bundle":"https://pith.science/pith/3MPJKXBSZD3B5R5JQMEANR4BX4/bundle.json","state":"https://pith.science/pith/3MPJKXBSZD3B5R5JQMEANR4BX4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3MPJKXBSZD3B5R5JQMEANR4BX4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3MPJKXBSZD3B5R5JQMEANR4BX4","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":"d973aa386254b3d9558f18488da123f9ce6df2f1271a1f5be13752eaaaf8f82d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T18:40:46Z","title_canon_sha256":"cbcc7c56a26e72b395ce556b8e56d81e2354759de0d567ed90816556eb9f212d"},"schema_version":"1.0","source":{"id":"2411.02372","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02372","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02372v2","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02372","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"pith_short_12","alias_value":"3MPJKXBSZD3B","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"pith_short_16","alias_value":"3MPJKXBSZD3B5R5J","created_at":"2026-07-05T10:22:09Z"},{"alias_kind":"pith_short_8","alias_value":"3MPJKXBS","created_at":"2026-07-05T10:22:09Z"}],"graph_snapshots":[{"event_id":"sha256:48cfa674d150e601b759589ca4a89f5acb9a68271b77cd6f99daaefb25f326e4","target":"graph","created_at":"2026-07-05T10:22:09Z","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/2411.02372/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a representation learning method that instead anticipates strong domain shifts at training time itself. We first propose a data engine that synthesizes highly variable training samples that would enable generalization to new biomedical contexts. To then train a single 3D network for any voxel-level task, we develop a contrastive learning method that pr","authors_text":"Adrian V. Dalca, Benjamin Billot, Clinton J. Wang, Hallee E. Wong, Mengwei Ren, Neel Dey, P. Ellen Grant, Polina Golland","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T18:40:46Z","title":"Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02372","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:ee0e94ceded06acb2a39dc2ccae80165b5da7e73cb5f4878c8fc1a08522640c3","target":"record","created_at":"2026-07-05T10:22:09Z","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":"d973aa386254b3d9558f18488da123f9ce6df2f1271a1f5be13752eaaaf8f82d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T18:40:46Z","title_canon_sha256":"cbcc7c56a26e72b395ce556b8e56d81e2354759de0d567ed90816556eb9f212d"},"schema_version":"1.0","source":{"id":"2411.02372","kind":"arxiv","version":2}},"canonical_sha256":"db1e955c32c8f61ec7a9830806c781bf3a970b5825516d858eb452e863414bc3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db1e955c32c8f61ec7a9830806c781bf3a970b5825516d858eb452e863414bc3","first_computed_at":"2026-07-05T10:22:09.376051Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:09.376051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KRzYDTevqS9hwmJzdmh6WSdB+sbD7S17g555fsNQ/Z5ANpSuGBC2U/Rp3Nr1Lujo6x+PlQcINOekcAArSs2zBg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:09.376645Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.02372","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee0e94ceded06acb2a39dc2ccae80165b5da7e73cb5f4878c8fc1a08522640c3","sha256:48cfa674d150e601b759589ca4a89f5acb9a68271b77cd6f99daaefb25f326e4"],"state_sha256":"10bd60ab96e26a8825132c47569d6837d363475d19a14f48083e537066c6c653"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nrir3YFgQeA+X1sUfFN6FQdJUKQdIY7t6RjV6pz4Tt7sWiLBgQze9m5Ogoz6uv+9ejtnZAZZgOog6MUc6r42Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T18:43:50.965926Z","bundle_sha256":"40c2ae3b7c16f4e052b6243acdf11b0e2ff7fc4db6d5e60ecec83fbbecdc69b3"}}