{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BWWA5FPJFAGJ6RTCW2VXMCJBJQ","short_pith_number":"pith:BWWA5FPJ","canonical_record":{"source":{"id":"2310.14230","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-22T08:46:40Z","cross_cats_sorted":[],"title_canon_sha256":"3fa0893a69a2066d40aff7bb1f78cbae99573ce68e1bb2e5351e7c5622d9678c","abstract_canon_sha256":"98f0ab469b209ed8981896dd4c707b7fdd7296aaf1ae459211dc4430f78de776"},"schema_version":"1.0"},"canonical_sha256":"0dac0e95e9280c9f4662b6ab7609214c30d087fde00c61e2eb145e37da8b0ffd","source":{"kind":"arxiv","id":"2310.14230","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14230","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14230v3","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14230","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"pith_short_12","alias_value":"BWWA5FPJFAGJ","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"pith_short_16","alias_value":"BWWA5FPJFAGJ6RTC","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"pith_short_8","alias_value":"BWWA5FPJ","created_at":"2026-07-05T07:55:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BWWA5FPJFAGJ6RTCW2VXMCJBJQ","target":"record","payload":{"canonical_record":{"source":{"id":"2310.14230","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-22T08:46:40Z","cross_cats_sorted":[],"title_canon_sha256":"3fa0893a69a2066d40aff7bb1f78cbae99573ce68e1bb2e5351e7c5622d9678c","abstract_canon_sha256":"98f0ab469b209ed8981896dd4c707b7fdd7296aaf1ae459211dc4430f78de776"},"schema_version":"1.0"},"canonical_sha256":"0dac0e95e9280c9f4662b6ab7609214c30d087fde00c61e2eb145e37da8b0ffd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:55:25.596086Z","signature_b64":"v2H5fHtJHmUp268GlLy9JMcFfLNGWjLTqQa4P+nwujJ8VHzIpFZ3+cepW6SZ5R7wp4mZKftLrgaU1VA7mswnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0dac0e95e9280c9f4662b6ab7609214c30d087fde00c61e2eb145e37da8b0ffd","last_reissued_at":"2026-07-05T07:55:25.595671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:55:25.595671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.14230","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:55:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CdL4u5HXi6xfbKFn7tXkMLyc7a/4IXIFkRfd7WgbuAEKzH31ICay8II2Ck6TKZhn+PGqa4Np3Vvt7dThYmiDBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T19:32:28.486921Z"},"content_sha256":"6e2fb5891edbec3a0464d0ebf411b29a02ab6325df3292e99e743b4ca13d3eef","schema_version":"1.0","event_id":"sha256:6e2fb5891edbec3a0464d0ebf411b29a02ab6325df3292e99e743b4ca13d3eef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BWWA5FPJFAGJ6RTCW2VXMCJBJQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A comprehensive survey on deep active learning in medical image analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoran Wang, Manning Wang, Qiuye Jin, Shiman Li, Siyu Liu, Zhijian Song","submitted_at":"2023-10-22T08:46:40Z","abstract_excerpt":"Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first tim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14230","kind":"arxiv","version":3},"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/2310.14230/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":"2026-07-05T07:55:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G6YHXQNDDrZ6ahy64H484O++O3KmXJjjfTZDApXh1eyY7kg86sKSVe0EqH0WGkEzj0SCImYAW+8XM45Nwj1QBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T19:32:28.487460Z"},"content_sha256":"3c85afafe3ccc8901dc7eaac9daae30c3552a4ee7191660c8ae31c980864aec8","schema_version":"1.0","event_id":"sha256:3c85afafe3ccc8901dc7eaac9daae30c3552a4ee7191660c8ae31c980864aec8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ/bundle.json","state_url":"https://pith.science/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ/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":"2026-08-18T19:32:28Z","links":{"resolver":"https://pith.science/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ","bundle":"https://pith.science/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ/bundle.json","state":"https://pith.science/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BWWA5FPJFAGJ6RTCW2VXMCJBJQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BWWA5FPJFAGJ6RTCW2VXMCJBJQ","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":"98f0ab469b209ed8981896dd4c707b7fdd7296aaf1ae459211dc4430f78de776","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-22T08:46:40Z","title_canon_sha256":"3fa0893a69a2066d40aff7bb1f78cbae99573ce68e1bb2e5351e7c5622d9678c"},"schema_version":"1.0","source":{"id":"2310.14230","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14230","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14230v3","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14230","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"pith_short_12","alias_value":"BWWA5FPJFAGJ","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"pith_short_16","alias_value":"BWWA5FPJFAGJ6RTC","created_at":"2026-07-05T07:55:25Z"},{"alias_kind":"pith_short_8","alias_value":"BWWA5FPJ","created_at":"2026-07-05T07:55:25Z"}],"graph_snapshots":[{"event_id":"sha256:3c85afafe3ccc8901dc7eaac9daae30c3552a4ee7191660c8ae31c980864aec8","target":"graph","created_at":"2026-07-05T07:55:25Z","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/2310.14230/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first tim","authors_text":"Haoran Wang, Manning Wang, Qiuye Jin, Shiman Li, Siyu Liu, Zhijian Song","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-22T08:46:40Z","title":"A comprehensive survey on deep active learning in medical image analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14230","kind":"arxiv","version":3},"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":"sha256:6e2fb5891edbec3a0464d0ebf411b29a02ab6325df3292e99e743b4ca13d3eef","target":"record","created_at":"2026-07-05T07:55:25Z","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":"98f0ab469b209ed8981896dd4c707b7fdd7296aaf1ae459211dc4430f78de776","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-22T08:46:40Z","title_canon_sha256":"3fa0893a69a2066d40aff7bb1f78cbae99573ce68e1bb2e5351e7c5622d9678c"},"schema_version":"1.0","source":{"id":"2310.14230","kind":"arxiv","version":3}},"canonical_sha256":"0dac0e95e9280c9f4662b6ab7609214c30d087fde00c61e2eb145e37da8b0ffd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0dac0e95e9280c9f4662b6ab7609214c30d087fde00c61e2eb145e37da8b0ffd","first_computed_at":"2026-07-05T07:55:25.595671Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:55:25.595671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v2H5fHtJHmUp268GlLy9JMcFfLNGWjLTqQa4P+nwujJ8VHzIpFZ3+cepW6SZ5R7wp4mZKftLrgaU1VA7mswnBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:55:25.596086Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.14230","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6e2fb5891edbec3a0464d0ebf411b29a02ab6325df3292e99e743b4ca13d3eef","sha256:3c85afafe3ccc8901dc7eaac9daae30c3552a4ee7191660c8ae31c980864aec8"],"state_sha256":"4b7e12c9fe3c3d560e23500d32a11d1b740b7d4c7da204bc3e2cdc8915fc6d33"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BSqDSXJ35hHSftUU0HX5mnnfOZ1Bz/LGPh4zLUOgvbn7Dq9CXyv5BQFK5wNQFb75QxzyTCu6vmTlipKEoMADAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T19:32:28.493381Z","bundle_sha256":"7726c176d554cd6ad0c3a33f1885aadc0d35d3aed88288beb61e338f60c1e608"}}