{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:JKS2ITWEHUIRCN6LZJVI7T4WCC","short_pith_number":"pith:JKS2ITWE","canonical_record":{"source":{"id":"1807.02420","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-06T13:57:48Z","cross_cats_sorted":[],"title_canon_sha256":"fb84a0c8f33ad23b11ab0ae86d7f9c40bd38deaddf7b4d21fc5ad16e6870b5bc","abstract_canon_sha256":"66a5adf72d7b022480df91c04900b25ef05a56f2764a135f08d5e81687a4362c"},"schema_version":"1.0"},"canonical_sha256":"4aa5a44ec43d111137cbca6a8fcf9610a3d65cb20d3a18c4033af573407a7130","source":{"kind":"arxiv","id":"1807.02420","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02420","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02420v1","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02420","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"JKS2ITWEHUIR","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JKS2ITWEHUIRCN6L","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JKS2ITWE","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:JKS2ITWEHUIRCN6LZJVI7T4WCC","target":"record","payload":{"canonical_record":{"source":{"id":"1807.02420","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-06T13:57:48Z","cross_cats_sorted":[],"title_canon_sha256":"fb84a0c8f33ad23b11ab0ae86d7f9c40bd38deaddf7b4d21fc5ad16e6870b5bc","abstract_canon_sha256":"66a5adf72d7b022480df91c04900b25ef05a56f2764a135f08d5e81687a4362c"},"schema_version":"1.0"},"canonical_sha256":"4aa5a44ec43d111137cbca6a8fcf9610a3d65cb20d3a18c4033af573407a7130","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:22.090932Z","signature_b64":"kul2YL4qMTkK5ISCulEg93f0mSiHuZqTL6fU4OeVePxFTffQwnT/TClUFNy0kdp0KwwxJqRqbCm1iftSbYQVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4aa5a44ec43d111137cbca6a8fcf9610a3d65cb20d3a18c4033af573407a7130","last_reissued_at":"2026-05-18T00:11:22.090211Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:22.090211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.02420","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":"2026-05-18T00:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gpp2Ww97EdZkXNggQVCpiieB5fexDfXNZ7RvQevkeNzYt45gdfpDtRNFBJchxpMRwCxOPvTOCnAf7boFwR/iCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T23:07:41.258784Z"},"content_sha256":"880f4ccb4b45f9629e937a02ca3d022deb7f8ab63fe0aac873015f735182bebe","schema_version":"1.0","event_id":"sha256:880f4ccb4b45f9629e937a02ca3d022deb7f8ab63fe0aac873015f735182bebe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:JKS2ITWEHUIRCN6LZJVI7T4WCC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reversed Active Learning based Atrous DenseNet for Pathological Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Linlin Shen, Shaoxiong Liu, Xinpeng Xie, Yuexiang Li","submitted_at":"2018-07-06T13:57:48Z","abstract_excerpt":"Witnessed the development of deep learning in recent years, increasing number of researches try to adopt deep learning model for medical image analysis. However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e.g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas. To address the challenges, we proposed a complete framework for the pathological image classification, which consists of a novel training strategy, namely reversed active learning (RAL), and an advanced network, namely atrous DenseNet (AD"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02420","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":""},"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":"2026-05-18T00:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dOVgx3VyrNeV9ttXJwksdGYcuuk2K+K9kE4zXM4ad3w6ZhswJBZkdo29iSu4FMd08mgPnmWYgYNIAPbgaIcjCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T23:07:41.259508Z"},"content_sha256":"282eab974160818592cb16a2bb5a76aa069427549ea7490cc9a1694f4ff8f6c8","schema_version":"1.0","event_id":"sha256:282eab974160818592cb16a2bb5a76aa069427549ea7490cc9a1694f4ff8f6c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC/bundle.json","state_url":"https://pith.science/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC/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":"2026-05-27T23:07:41Z","links":{"resolver":"https://pith.science/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC","bundle":"https://pith.science/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC/bundle.json","state":"https://pith.science/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JKS2ITWEHUIRCN6LZJVI7T4WCC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JKS2ITWEHUIRCN6LZJVI7T4WCC","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":"66a5adf72d7b022480df91c04900b25ef05a56f2764a135f08d5e81687a4362c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-06T13:57:48Z","title_canon_sha256":"fb84a0c8f33ad23b11ab0ae86d7f9c40bd38deaddf7b4d21fc5ad16e6870b5bc"},"schema_version":"1.0","source":{"id":"1807.02420","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02420","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02420v1","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02420","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"JKS2ITWEHUIR","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JKS2ITWEHUIRCN6L","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JKS2ITWE","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:282eab974160818592cb16a2bb5a76aa069427549ea7490cc9a1694f4ff8f6c8","target":"graph","created_at":"2026-05-18T00:11:22Z","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":"Witnessed the development of deep learning in recent years, increasing number of researches try to adopt deep learning model for medical image analysis. However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e.g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas. To address the challenges, we proposed a complete framework for the pathological image classification, which consists of a novel training strategy, namely reversed active learning (RAL), and an advanced network, namely atrous DenseNet (AD","authors_text":"Linlin Shen, Shaoxiong Liu, Xinpeng Xie, Yuexiang Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-06T13:57:48Z","title":"Reversed Active Learning based Atrous DenseNet for Pathological Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02420","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":"sha256:880f4ccb4b45f9629e937a02ca3d022deb7f8ab63fe0aac873015f735182bebe","target":"record","created_at":"2026-05-18T00:11:22Z","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":"66a5adf72d7b022480df91c04900b25ef05a56f2764a135f08d5e81687a4362c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-06T13:57:48Z","title_canon_sha256":"fb84a0c8f33ad23b11ab0ae86d7f9c40bd38deaddf7b4d21fc5ad16e6870b5bc"},"schema_version":"1.0","source":{"id":"1807.02420","kind":"arxiv","version":1}},"canonical_sha256":"4aa5a44ec43d111137cbca6a8fcf9610a3d65cb20d3a18c4033af573407a7130","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4aa5a44ec43d111137cbca6a8fcf9610a3d65cb20d3a18c4033af573407a7130","first_computed_at":"2026-05-18T00:11:22.090211Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:22.090211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kul2YL4qMTkK5ISCulEg93f0mSiHuZqTL6fU4OeVePxFTffQwnT/TClUFNy0kdp0KwwxJqRqbCm1iftSbYQVDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:22.090932Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.02420","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:880f4ccb4b45f9629e937a02ca3d022deb7f8ab63fe0aac873015f735182bebe","sha256:282eab974160818592cb16a2bb5a76aa069427549ea7490cc9a1694f4ff8f6c8"],"state_sha256":"4f24ab985b4335a9e669b48663e4b04702d80dfec4dba843f8dc3c333454db79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dfc08FWw+zRBGQ+IeDpM5i5/AjXtSi7hlzUH/Z04pa2/1J1Py2rSG3eCWzTxDUlxAzmVjNAqfttd2JmsKPNIBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T23:07:41.263087Z","bundle_sha256":"ec367aa927361a1b33c4510e5b802744728be4347a00f4bde54ed863d9066e3f"}}