{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7RPZM4MIAO4J4CFFTDPIDSEZ25","short_pith_number":"pith:7RPZM4MI","canonical_record":{"source":{"id":"1805.06983","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T22:43:46Z","cross_cats_sorted":[],"title_canon_sha256":"e756a2e8b6d92af3801d00915e82247748e101b71d4c6a364b868507e5720e99","abstract_canon_sha256":"b8d80fa616a6671103e92cb9a1dbd06bec98ed8b2bcbe866f0a603a3f462c8a1"},"schema_version":"1.0"},"canonical_sha256":"fc5f96718803b89e08a598de81c899d7504bec9c309d4ae00e941dedae8e244b","source":{"kind":"arxiv","id":"1805.06983","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06983","created_at":"2026-05-18T00:04:35Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06983v2","created_at":"2026-05-18T00:04:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06983","created_at":"2026-05-18T00:04:35Z"},{"alias_kind":"pith_short_12","alias_value":"7RPZM4MIAO4J","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7RPZM4MIAO4J4CFF","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7RPZM4MI","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7RPZM4MIAO4J4CFFTDPIDSEZ25","target":"record","payload":{"canonical_record":{"source":{"id":"1805.06983","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T22:43:46Z","cross_cats_sorted":[],"title_canon_sha256":"e756a2e8b6d92af3801d00915e82247748e101b71d4c6a364b868507e5720e99","abstract_canon_sha256":"b8d80fa616a6671103e92cb9a1dbd06bec98ed8b2bcbe866f0a603a3f462c8a1"},"schema_version":"1.0"},"canonical_sha256":"fc5f96718803b89e08a598de81c899d7504bec9c309d4ae00e941dedae8e244b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:35.594969Z","signature_b64":"gMIBSbrQvY18AGLYy+hMPHSLeojEgbKAzIJJbYKRwN/sRZrf9sOyKQn0W5Y7MvsD3a9BVxZ1kvNj2HSlISXvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc5f96718803b89e08a598de81c899d7504bec9c309d4ae00e941dedae8e244b","last_reissued_at":"2026-05-18T00:04:35.594413Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:35.594413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.06983","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":"2026-05-18T00:04:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sZFUpSaFHWt246krfrUwxJs45CYPiobcZ2R5arV0DyEVNhicJzfIwl3U88hzESgOeGOUDkDzP2TNhGhNUihXCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:56:01.577685Z"},"content_sha256":"f11d4dad5551ab24cb32c11bd3d9c164a88d5a4847c498434be6845830c98d80","schema_version":"1.0","event_id":"sha256:f11d4dad5551ab24cb32c11bd3d9c164a88d5a4847c498434be6845830c98d80"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7RPZM4MIAO4J4CFFTDPIDSEZ25","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gabriele Campanella, Thomas J. Fuchs, Vitor Werneck Krauss Silva","submitted_at":"2018-05-17T22:43:46Z","abstract_excerpt":"In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets. Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic. Here, we have gathered a dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset. Given the size of our dataset it is possible for us "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06983","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":""},"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":"2026-05-18T00:04:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eIbGKgpmmjbOseigtiV5UOeS5F9ZWeVBWrgdIabXU8Klw4tEJUXWYDH2PolfHuZ6IYwuRADqr7R967l3GpQVAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:56:01.578103Z"},"content_sha256":"c62f332c0225da40093586a3a8b68b97c29d751271ed363ea3e358a129fc8f23","schema_version":"1.0","event_id":"sha256:c62f332c0225da40093586a3a8b68b97c29d751271ed363ea3e358a129fc8f23"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25/bundle.json","state_url":"https://pith.science/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25/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":"2026-05-26T11:56:01Z","links":{"resolver":"https://pith.science/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25","bundle":"https://pith.science/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25/bundle.json","state":"https://pith.science/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7RPZM4MIAO4J4CFFTDPIDSEZ25/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7RPZM4MIAO4J4CFFTDPIDSEZ25","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":"b8d80fa616a6671103e92cb9a1dbd06bec98ed8b2bcbe866f0a603a3f462c8a1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T22:43:46Z","title_canon_sha256":"e756a2e8b6d92af3801d00915e82247748e101b71d4c6a364b868507e5720e99"},"schema_version":"1.0","source":{"id":"1805.06983","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06983","created_at":"2026-05-18T00:04:35Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06983v2","created_at":"2026-05-18T00:04:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06983","created_at":"2026-05-18T00:04:35Z"},{"alias_kind":"pith_short_12","alias_value":"7RPZM4MIAO4J","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7RPZM4MIAO4J4CFF","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7RPZM4MI","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:c62f332c0225da40093586a3a8b68b97c29d751271ed363ea3e358a129fc8f23","target":"graph","created_at":"2026-05-18T00:04:35Z","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":"In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets. Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic. Here, we have gathered a dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset. Given the size of our dataset it is possible for us ","authors_text":"Gabriele Campanella, Thomas J. Fuchs, Vitor Werneck Krauss Silva","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T22:43:46Z","title":"Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06983","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:f11d4dad5551ab24cb32c11bd3d9c164a88d5a4847c498434be6845830c98d80","target":"record","created_at":"2026-05-18T00:04:35Z","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":"b8d80fa616a6671103e92cb9a1dbd06bec98ed8b2bcbe866f0a603a3f462c8a1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T22:43:46Z","title_canon_sha256":"e756a2e8b6d92af3801d00915e82247748e101b71d4c6a364b868507e5720e99"},"schema_version":"1.0","source":{"id":"1805.06983","kind":"arxiv","version":2}},"canonical_sha256":"fc5f96718803b89e08a598de81c899d7504bec9c309d4ae00e941dedae8e244b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc5f96718803b89e08a598de81c899d7504bec9c309d4ae00e941dedae8e244b","first_computed_at":"2026-05-18T00:04:35.594413Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:35.594413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gMIBSbrQvY18AGLYy+hMPHSLeojEgbKAzIJJbYKRwN/sRZrf9sOyKQn0W5Y7MvsD3a9BVxZ1kvNj2HSlISXvAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:35.594969Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.06983","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f11d4dad5551ab24cb32c11bd3d9c164a88d5a4847c498434be6845830c98d80","sha256:c62f332c0225da40093586a3a8b68b97c29d751271ed363ea3e358a129fc8f23"],"state_sha256":"04bc46a9795e4d90d04f808fe7075c4350a1ba5001fad290ce26951012bd34fc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3RTvTQb24kq14dQDLbildsvJV4Fr6dr6ZutYWJvc/Xpr62jojBhZp8Qs0/qWHGg7tfZHZ0OTHl3GXbQWIw2WBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:56:01.581760Z","bundle_sha256":"4ea50cc282ad1b413685a881f4f71ccb11ed0c685541afcf4bb01beadc88f188"}}