{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:B3XZN7XHOJL6UW34OHQKDB7Z45","short_pith_number":"pith:B3XZN7XH","canonical_record":{"source":{"id":"1803.00386","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-12T12:42:27Z","cross_cats_sorted":[],"title_canon_sha256":"68d3d827c9033eadfca6a5389a8539dd9cbf37e9cbd8c9b003d760919ff9f84a","abstract_canon_sha256":"74fcccaf2f04ac97c0c45800b7becfdf674c89089476ec6a03e25cd001678a39"},"schema_version":"1.0"},"canonical_sha256":"0eef96fee77257ea5b7c71e0a187f9e7754d6fbcbdace0a2ed8e7b28cfafb242","source":{"kind":"arxiv","id":"1803.00386","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.00386","created_at":"2026-05-18T00:21:56Z"},{"alias_kind":"arxiv_version","alias_value":"1803.00386v2","created_at":"2026-05-18T00:21:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00386","created_at":"2026-05-18T00:21:56Z"},{"alias_kind":"pith_short_12","alias_value":"B3XZN7XHOJL6","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"B3XZN7XHOJL6UW34","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"B3XZN7XH","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:B3XZN7XHOJL6UW34OHQKDB7Z45","target":"record","payload":{"canonical_record":{"source":{"id":"1803.00386","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-12T12:42:27Z","cross_cats_sorted":[],"title_canon_sha256":"68d3d827c9033eadfca6a5389a8539dd9cbf37e9cbd8c9b003d760919ff9f84a","abstract_canon_sha256":"74fcccaf2f04ac97c0c45800b7becfdf674c89089476ec6a03e25cd001678a39"},"schema_version":"1.0"},"canonical_sha256":"0eef96fee77257ea5b7c71e0a187f9e7754d6fbcbdace0a2ed8e7b28cfafb242","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:56.526757Z","signature_b64":"DShyd1kSNH4SWIhqJfbeb35gHcLfTI9L/x04PRkTdGvjcL4t7VfAzORBStZBYJ7SyqyHsi+uSpCu90RqiSggAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0eef96fee77257ea5b7c71e0a187f9e7754d6fbcbdace0a2ed8e7b28cfafb242","last_reissued_at":"2026-05-18T00:21:56.526106Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:56.526106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.00386","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":"2026-05-18T00:21:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OgRadB0ZfPZytp/kKxr2OVLOhoZD3ACwKWf8Ttgb98Rqb9ozsXSzKE2vShFXw//y5uLE6ghWFvAGavayy9+mBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:41:21.497042Z"},"content_sha256":"c65e773de1aa677f34f5950e5cfd29b7ac800c9c245c89383531c5966fb7aad7","schema_version":"1.0","event_id":"sha256:c65e773de1aa677f34f5950e5cfd29b7ac800c9c245c89383531c5966fb7aad7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:B3XZN7XHOJL6UW34OHQKDB7Z45","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Lisowska, Muhammad Shaban, Nasir Rajpoot, Navid Alemi Koohbanani, Ruqayya Awan","submitted_at":"2018-02-12T12:42:27Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for training a CNN which limits its use by the computational pathology community. In previous studies, CNNs have demonstrated their potential in terms of feature generalizability and transferability accompanied with better performance. Considering these traits of CNN, we propose a simple yet effective method which leverages the strengths of CNN combined with the advantages of including contextual information, particularly desig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00386","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":"2026-05-18T00:21:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OQgLqLsEVk0jXyoC4rDsYEqjM0yHh0AsRi8nECnH/0U4Cxxpd2Eh7HaOlJC0Sc1dcZn5saYsbnT0bBqwz2uPCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:41:21.497418Z"},"content_sha256":"4e63324e62b3d1230f20f34e4b92208a3378fe20d990b1ee11cff4dba719c10f","schema_version":"1.0","event_id":"sha256:4e63324e62b3d1230f20f34e4b92208a3378fe20d990b1ee11cff4dba719c10f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B3XZN7XHOJL6UW34OHQKDB7Z45/bundle.json","state_url":"https://pith.science/pith/B3XZN7XHOJL6UW34OHQKDB7Z45/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B3XZN7XHOJL6UW34OHQKDB7Z45/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":"2026-05-27T02:41:21Z","links":{"resolver":"https://pith.science/pith/B3XZN7XHOJL6UW34OHQKDB7Z45","bundle":"https://pith.science/pith/B3XZN7XHOJL6UW34OHQKDB7Z45/bundle.json","state":"https://pith.science/pith/B3XZN7XHOJL6UW34OHQKDB7Z45/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B3XZN7XHOJL6UW34OHQKDB7Z45/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:B3XZN7XHOJL6UW34OHQKDB7Z45","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":"74fcccaf2f04ac97c0c45800b7becfdf674c89089476ec6a03e25cd001678a39","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-12T12:42:27Z","title_canon_sha256":"68d3d827c9033eadfca6a5389a8539dd9cbf37e9cbd8c9b003d760919ff9f84a"},"schema_version":"1.0","source":{"id":"1803.00386","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.00386","created_at":"2026-05-18T00:21:56Z"},{"alias_kind":"arxiv_version","alias_value":"1803.00386v2","created_at":"2026-05-18T00:21:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00386","created_at":"2026-05-18T00:21:56Z"},{"alias_kind":"pith_short_12","alias_value":"B3XZN7XHOJL6","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"B3XZN7XHOJL6UW34","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"B3XZN7XH","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:4e63324e62b3d1230f20f34e4b92208a3378fe20d990b1ee11cff4dba719c10f","target":"graph","created_at":"2026-05-18T00:21:56Z","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":"Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for training a CNN which limits its use by the computational pathology community. In previous studies, CNNs have demonstrated their potential in terms of feature generalizability and transferability accompanied with better performance. Considering these traits of CNN, we propose a simple yet effective method which leverages the strengths of CNN combined with the advantages of including contextual information, particularly desig","authors_text":"Anna Lisowska, Muhammad Shaban, Nasir Rajpoot, Navid Alemi Koohbanani, Ruqayya Awan","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-12T12:42:27Z","title":"Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00386","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:c65e773de1aa677f34f5950e5cfd29b7ac800c9c245c89383531c5966fb7aad7","target":"record","created_at":"2026-05-18T00:21:56Z","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":"74fcccaf2f04ac97c0c45800b7becfdf674c89089476ec6a03e25cd001678a39","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-12T12:42:27Z","title_canon_sha256":"68d3d827c9033eadfca6a5389a8539dd9cbf37e9cbd8c9b003d760919ff9f84a"},"schema_version":"1.0","source":{"id":"1803.00386","kind":"arxiv","version":2}},"canonical_sha256":"0eef96fee77257ea5b7c71e0a187f9e7754d6fbcbdace0a2ed8e7b28cfafb242","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0eef96fee77257ea5b7c71e0a187f9e7754d6fbcbdace0a2ed8e7b28cfafb242","first_computed_at":"2026-05-18T00:21:56.526106Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:56.526106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DShyd1kSNH4SWIhqJfbeb35gHcLfTI9L/x04PRkTdGvjcL4t7VfAzORBStZBYJ7SyqyHsi+uSpCu90RqiSggAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:56.526757Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.00386","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c65e773de1aa677f34f5950e5cfd29b7ac800c9c245c89383531c5966fb7aad7","sha256:4e63324e62b3d1230f20f34e4b92208a3378fe20d990b1ee11cff4dba719c10f"],"state_sha256":"fc77728533d893781ce3bd1d9df095bff355d5a319f34c9891c4988640427a88"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q8zAVdXd/bN+Zxw7FxaBfIxKvx2jxxp8aw4HrJiQaQSfIXMQvzoWhg2AgK8Pnc86p7GnFGmdzxEh86LNLaOGCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T02:41:21.500219Z","bundle_sha256":"ecd545bf9a24f6886f546c4f6357974fb2e2e78be647f3ee75096aa87c051ced"}}