{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:OZFH7CK5P4PVKDYOHGNIVMMPMZ","short_pith_number":"pith:OZFH7CK5","canonical_record":{"source":{"id":"2210.17013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-31T02:06:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7f4dfcc3290b93e2aec5fdd73476c02bb52c1feb19abddfe00aa3a741fef7bb3","abstract_canon_sha256":"fab8110db46f3c01a59867cf5f2af462f16a758d5fb382133c2002694b75d0cb"},"schema_version":"1.0"},"canonical_sha256":"764a7f895d7f1f550f0e399a8ab18f667144d685156eb6bde158258c0cef8797","source":{"kind":"arxiv","id":"2210.17013","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.17013","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"arxiv_version","alias_value":"2210.17013v1","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.17013","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"pith_short_12","alias_value":"OZFH7CK5P4PV","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"pith_short_16","alias_value":"OZFH7CK5P4PVKDYO","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"pith_short_8","alias_value":"OZFH7CK5","created_at":"2026-07-05T05:11:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:OZFH7CK5P4PVKDYOHGNIVMMPMZ","target":"record","payload":{"canonical_record":{"source":{"id":"2210.17013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-31T02:06:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7f4dfcc3290b93e2aec5fdd73476c02bb52c1feb19abddfe00aa3a741fef7bb3","abstract_canon_sha256":"fab8110db46f3c01a59867cf5f2af462f16a758d5fb382133c2002694b75d0cb"},"schema_version":"1.0"},"canonical_sha256":"764a7f895d7f1f550f0e399a8ab18f667144d685156eb6bde158258c0cef8797","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:53.897154Z","signature_b64":"t3uu6Ysp3dZW26XTH0JQ28bb48vMv8l7t5PQ0xZ6CvX+GHZ2rGVmDYzCFM713EWLb2srbuCB5UiP2o99qe6aDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"764a7f895d7f1f550f0e399a8ab18f667144d685156eb6bde158258c0cef8797","last_reissued_at":"2026-07-05T05:11:53.896690Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:53.896690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.17013","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":"2026-07-05T05:11:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hEBooBOccSmAKMc7o+4XJfFOWfhtuIy9YkXt87zDdk/Lj1GP29EIebFCBVULFQ0KuvK/VP02f1SzVNRRnT2YBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:39:31.566976Z"},"content_sha256":"2226096476f05c001ca5237dd5995f55c09a82c4e7ea1de8095a3036cdf4cb2e","schema_version":"1.0","event_id":"sha256:2226096476f05c001ca5237dd5995f55c09a82c4e7ea1de8095a3036cdf4cb2e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:OZFH7CK5P4PVKDYOHGNIVMMPMZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Faisal Mahmood, Guillaume Jaume, Imaad Zaffar, Nasir Rajpoot","submitted_at":"2022-10-31T02:06:39Z","abstract_excerpt":"Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.17013","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/2210.17013/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":"2026-07-05T05:11:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c+l1TCpReS3o7gO+zmuJ2yw8bS+LfBAqB4tNV1dfUgp2GQJ9FSUwgP4+x7e1auMpXT0f1lf/BC3lN+m9PNx7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:39:31.567766Z"},"content_sha256":"7590f2aa25480a0da9d5c73ab46e40b9cc9e398c73666cd051bf04c3f150e566","schema_version":"1.0","event_id":"sha256:7590f2aa25480a0da9d5c73ab46e40b9cc9e398c73666cd051bf04c3f150e566"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ/bundle.json","state_url":"https://pith.science/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ/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":"2026-08-13T20:39:31Z","links":{"resolver":"https://pith.science/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ","bundle":"https://pith.science/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ/bundle.json","state":"https://pith.science/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OZFH7CK5P4PVKDYOHGNIVMMPMZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:OZFH7CK5P4PVKDYOHGNIVMMPMZ","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":"fab8110db46f3c01a59867cf5f2af462f16a758d5fb382133c2002694b75d0cb","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-31T02:06:39Z","title_canon_sha256":"7f4dfcc3290b93e2aec5fdd73476c02bb52c1feb19abddfe00aa3a741fef7bb3"},"schema_version":"1.0","source":{"id":"2210.17013","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.17013","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"arxiv_version","alias_value":"2210.17013v1","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.17013","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"pith_short_12","alias_value":"OZFH7CK5P4PV","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"pith_short_16","alias_value":"OZFH7CK5P4PVKDYO","created_at":"2026-07-05T05:11:53Z"},{"alias_kind":"pith_short_8","alias_value":"OZFH7CK5","created_at":"2026-07-05T05:11:53Z"}],"graph_snapshots":[{"event_id":"sha256:7590f2aa25480a0da9d5c73ab46e40b9cc9e398c73666cd051bf04c3f150e566","target":"graph","created_at":"2026-07-05T05:11:53Z","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/2210.17013/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than ","authors_text":"Faisal Mahmood, Guillaume Jaume, Imaad Zaffar, Nasir Rajpoot","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-31T02:06:39Z","title":"Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.17013","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":"sha256:2226096476f05c001ca5237dd5995f55c09a82c4e7ea1de8095a3036cdf4cb2e","target":"record","created_at":"2026-07-05T05:11:53Z","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":"fab8110db46f3c01a59867cf5f2af462f16a758d5fb382133c2002694b75d0cb","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-31T02:06:39Z","title_canon_sha256":"7f4dfcc3290b93e2aec5fdd73476c02bb52c1feb19abddfe00aa3a741fef7bb3"},"schema_version":"1.0","source":{"id":"2210.17013","kind":"arxiv","version":1}},"canonical_sha256":"764a7f895d7f1f550f0e399a8ab18f667144d685156eb6bde158258c0cef8797","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"764a7f895d7f1f550f0e399a8ab18f667144d685156eb6bde158258c0cef8797","first_computed_at":"2026-07-05T05:11:53.896690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:11:53.896690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t3uu6Ysp3dZW26XTH0JQ28bb48vMv8l7t5PQ0xZ6CvX+GHZ2rGVmDYzCFM713EWLb2srbuCB5UiP2o99qe6aDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:11:53.897154Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.17013","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2226096476f05c001ca5237dd5995f55c09a82c4e7ea1de8095a3036cdf4cb2e","sha256:7590f2aa25480a0da9d5c73ab46e40b9cc9e398c73666cd051bf04c3f150e566"],"state_sha256":"34cbe62407e988c743781e3f6b0b30761c6902fe36c1222e6f76df07bbd950ab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/XJUS/ZILu8rWPEy1Tec7fLFRvNo2YelMwskeOg4ZSVovGtuQ9shel7mf3O2kCPWsaqhf+oYeF6udeqpa6epDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T20:39:31.578721Z","bundle_sha256":"1427d6c7b468164fea26cf22850eb80ee32e03448c7344d69fe07e5921a20ded"}}