{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CAZXP7OCIXLDXF7ZQ2AUQM73CP","short_pith_number":"pith:CAZXP7OC","canonical_record":{"source":{"id":"2403.04865","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-07T19:28:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9ee0044f1ba557a8f55493335bb298cabbb670ce7ed96745bc5f979c1874e964","abstract_canon_sha256":"5883aeb2ddcccccb12e825146da28169001029499510966fad87ec377d8f26a9"},"schema_version":"1.0"},"canonical_sha256":"103377fdc245d63b97f986814833fb13e9e37a5bda31b7feb521bc3fdd2a9433","source":{"kind":"arxiv","id":"2403.04865","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.04865","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"2403.04865v2","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04865","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"CAZXP7OCIXLD","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"pith_short_16","alias_value":"CAZXP7OCIXLDXF7Z","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"pith_short_8","alias_value":"CAZXP7OC","created_at":"2026-07-05T08:21:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CAZXP7OCIXLDXF7ZQ2AUQM73CP","target":"record","payload":{"canonical_record":{"source":{"id":"2403.04865","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-07T19:28:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9ee0044f1ba557a8f55493335bb298cabbb670ce7ed96745bc5f979c1874e964","abstract_canon_sha256":"5883aeb2ddcccccb12e825146da28169001029499510966fad87ec377d8f26a9"},"schema_version":"1.0"},"canonical_sha256":"103377fdc245d63b97f986814833fb13e9e37a5bda31b7feb521bc3fdd2a9433","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:51.308732Z","signature_b64":"PwlYwRLL+AaN+VbMHiu6EA3WIBnDfMAYYWxt1Jig2oO+czMyCcqsIsn5Q5fsbXkGau//NUU4XgrT8ACXEZCWAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"103377fdc245d63b97f986814833fb13e9e37a5bda31b7feb521bc3fdd2a9433","last_reissued_at":"2026-07-05T08:21:51.308191Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:51.308191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.04865","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":"2026-07-05T08:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KZIRq3gUrTkJwuphj02QafFHtywbf9y6cfc7GRm8vVE6Nd42P7RWKTWLNLibyKlGDRE+AXu3kC24ic+ra3MeCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:23:18.391066Z"},"content_sha256":"5151b17272f35e412bddf9989afad41c9066fc901112ece586ece003798aeaeb","schema_version":"1.0","event_id":"sha256:5151b17272f35e412bddf9989afad41c9066fc901112ece586ece003798aeaeb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CAZXP7OCIXLDXF7ZQ2AUQM73CP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chad Vanderbilt, Eugene Fluder, Gabriele Campanella, Jennifer Zeng, Thomas J. Fuchs","submitted_at":"2024-03-07T19:28:58Z","abstract_excerpt":"Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets. Computational Pathology, with its massive amounts of microscopy image data and impact on diagnostics and biomarkers, is at the forefront of this development. Gigapixel pathology slides pose a unique challenge due to their enormous size and are usually divided into tens of thousands of smaller tiles for analysis. This results in a discontinuity in the machine learning process by separating the training of tile-level encoders from slide-level aggregators and the ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04865","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/2403.04865/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":"2026-07-05T08:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rqWepwC7IJkKdORniOS/5QPbuekEdGbOhporfK99YePiI2q1PXP0OV1i/IjrFxlUPyaN4qJUpM27E/JpNwmVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:23:18.391446Z"},"content_sha256":"d69445fdf680ce5778288ff30b39b445e995d922a5b51c26eab4c95a2f99119f","schema_version":"1.0","event_id":"sha256:d69445fdf680ce5778288ff30b39b445e995d922a5b51c26eab4c95a2f99119f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP/bundle.json","state_url":"https://pith.science/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP/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":"2026-07-07T08:23:18Z","links":{"resolver":"https://pith.science/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP","bundle":"https://pith.science/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP/bundle.json","state":"https://pith.science/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CAZXP7OCIXLDXF7ZQ2AUQM73CP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CAZXP7OCIXLDXF7ZQ2AUQM73CP","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":"5883aeb2ddcccccb12e825146da28169001029499510966fad87ec377d8f26a9","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-07T19:28:58Z","title_canon_sha256":"9ee0044f1ba557a8f55493335bb298cabbb670ce7ed96745bc5f979c1874e964"},"schema_version":"1.0","source":{"id":"2403.04865","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.04865","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"2403.04865v2","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04865","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"CAZXP7OCIXLD","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"pith_short_16","alias_value":"CAZXP7OCIXLDXF7Z","created_at":"2026-07-05T08:21:51Z"},{"alias_kind":"pith_short_8","alias_value":"CAZXP7OC","created_at":"2026-07-05T08:21:51Z"}],"graph_snapshots":[{"event_id":"sha256:d69445fdf680ce5778288ff30b39b445e995d922a5b51c26eab4c95a2f99119f","target":"graph","created_at":"2026-07-05T08:21:51Z","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/2403.04865/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets. Computational Pathology, with its massive amounts of microscopy image data and impact on diagnostics and biomarkers, is at the forefront of this development. Gigapixel pathology slides pose a unique challenge due to their enormous size and are usually divided into tens of thousands of smaller tiles for analysis. This results in a discontinuity in the machine learning process by separating the training of tile-level encoders from slide-level aggregators and the ne","authors_text":"Chad Vanderbilt, Eugene Fluder, Gabriele Campanella, Jennifer Zeng, Thomas J. Fuchs","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-07T19:28:58Z","title":"Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04865","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":"sha256:5151b17272f35e412bddf9989afad41c9066fc901112ece586ece003798aeaeb","target":"record","created_at":"2026-07-05T08:21:51Z","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":"5883aeb2ddcccccb12e825146da28169001029499510966fad87ec377d8f26a9","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-07T19:28:58Z","title_canon_sha256":"9ee0044f1ba557a8f55493335bb298cabbb670ce7ed96745bc5f979c1874e964"},"schema_version":"1.0","source":{"id":"2403.04865","kind":"arxiv","version":2}},"canonical_sha256":"103377fdc245d63b97f986814833fb13e9e37a5bda31b7feb521bc3fdd2a9433","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"103377fdc245d63b97f986814833fb13e9e37a5bda31b7feb521bc3fdd2a9433","first_computed_at":"2026-07-05T08:21:51.308191Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:21:51.308191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PwlYwRLL+AaN+VbMHiu6EA3WIBnDfMAYYWxt1Jig2oO+czMyCcqsIsn5Q5fsbXkGau//NUU4XgrT8ACXEZCWAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:21:51.308732Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.04865","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5151b17272f35e412bddf9989afad41c9066fc901112ece586ece003798aeaeb","sha256:d69445fdf680ce5778288ff30b39b445e995d922a5b51c26eab4c95a2f99119f"],"state_sha256":"708466ba1556bef4546a8a8ab637a1fff28b37a503ac4efc2451da3a9e583748"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5tjj1Esl9wzjuoKlwhAqFj2eiZbbR8T3soCf+NN5N/ABy731Q+s+yEWTpIdMq22M3dnh6g0aV5OzA09k1fptBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:23:18.393422Z","bundle_sha256":"e827fe89ccd122513ffc231b731acb1982c226b0e643bdac6923cee922f08624"}}