{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:MOW4JVX4A2ZV37VHMAEPWA3BRW","short_pith_number":"pith:MOW4JVX4","canonical_record":{"source":{"id":"1807.10819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-27T20:10:11Z","cross_cats_sorted":[],"title_canon_sha256":"c0f04b13549199763c998e8ea9862162b2ee52a8cf85578a7e714167fffa0e31","abstract_canon_sha256":"6fb358ce4387d9b3b3adad89f94abb774a5fe58fdae63bc09fd36b32d0ccd878"},"schema_version":"1.0"},"canonical_sha256":"63adc4d6fc06b35dfea76008fb03618db99166d3e30a1654833f94b41bb4fb2f","source":{"kind":"arxiv","id":"1807.10819","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10819","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10819v1","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10819","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"MOW4JVX4A2ZV","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"MOW4JVX4A2ZV37VH","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"MOW4JVX4","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:MOW4JVX4A2ZV37VHMAEPWA3BRW","target":"record","payload":{"canonical_record":{"source":{"id":"1807.10819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-27T20:10:11Z","cross_cats_sorted":[],"title_canon_sha256":"c0f04b13549199763c998e8ea9862162b2ee52a8cf85578a7e714167fffa0e31","abstract_canon_sha256":"6fb358ce4387d9b3b3adad89f94abb774a5fe58fdae63bc09fd36b32d0ccd878"},"schema_version":"1.0"},"canonical_sha256":"63adc4d6fc06b35dfea76008fb03618db99166d3e30a1654833f94b41bb4fb2f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:37.313477Z","signature_b64":"KV8LGVekWyMWR20Dtjt4bGzJpnrZqU1fZZK+8wgBYlWF6fXREwzhgXq8wNh3N1WjwfwoDZP80D1X2ESt7rYOCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63adc4d6fc06b35dfea76008fb03618db99166d3e30a1654833f94b41bb4fb2f","last_reissued_at":"2026-05-18T00:09:37.312962Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:37.312962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.10819","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":"2026-05-18T00:09:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ScxKAQQJvjxuO0BUk2Rop25HqVrpBw02hJr91Upk5WUs32zV+WJjj7MMWZHEarzkLQJG3iG383CxV0n6uV9lCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:36:14.054720Z"},"content_sha256":"11ed7cd5bc2e5ed71d9e67b08f54686680d70050b07162a52b6448c8247cb4a1","schema_version":"1.0","event_id":"sha256:11ed7cd5bc2e5ed71d9e67b08f54686680d70050b07162a52b6448c8247cb4a1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:MOW4JVX4A2ZV37VHMAEPWA3BRW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Jesson, Damien Goblot, Florian Soudan, Nicolas Chapados, Nicolas Guizard, Sina Hamidi Ghalehjegh","submitted_at":"2018-07-27T20:10:11Z","abstract_excerpt":"We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10819","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":"2026-05-18T00:09:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QEHbhXL4G4rNhEueUtvOmLzN7u7Lc3x+RJiiQlbGqr3jg5sx9Rm5tGmE01Ix8UQqLakzOf2g7rg+ldVhzLzDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:36:14.055335Z"},"content_sha256":"33ab04b438739c94eef77dcf5bc2aee776e708cb96d5a8d0df1b35b14bbb3618","schema_version":"1.0","event_id":"sha256:33ab04b438739c94eef77dcf5bc2aee776e708cb96d5a8d0df1b35b14bbb3618"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW/bundle.json","state_url":"https://pith.science/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW/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":"2026-05-26T11:36:14Z","links":{"resolver":"https://pith.science/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW","bundle":"https://pith.science/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW/bundle.json","state":"https://pith.science/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MOW4JVX4A2ZV37VHMAEPWA3BRW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:MOW4JVX4A2ZV37VHMAEPWA3BRW","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":"6fb358ce4387d9b3b3adad89f94abb774a5fe58fdae63bc09fd36b32d0ccd878","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-27T20:10:11Z","title_canon_sha256":"c0f04b13549199763c998e8ea9862162b2ee52a8cf85578a7e714167fffa0e31"},"schema_version":"1.0","source":{"id":"1807.10819","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10819","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10819v1","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10819","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"MOW4JVX4A2ZV","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"MOW4JVX4A2ZV37VH","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"MOW4JVX4","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:33ab04b438739c94eef77dcf5bc2aee776e708cb96d5a8d0df1b35b14bbb3618","target":"graph","created_at":"2026-05-18T00:09:37Z","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":"We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED im","authors_text":"Andrew Jesson, Damien Goblot, Florian Soudan, Nicolas Chapados, Nicolas Guizard, Sina Hamidi Ghalehjegh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-27T20:10:11Z","title":"CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10819","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":"sha256:11ed7cd5bc2e5ed71d9e67b08f54686680d70050b07162a52b6448c8247cb4a1","target":"record","created_at":"2026-05-18T00:09:37Z","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":"6fb358ce4387d9b3b3adad89f94abb774a5fe58fdae63bc09fd36b32d0ccd878","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-27T20:10:11Z","title_canon_sha256":"c0f04b13549199763c998e8ea9862162b2ee52a8cf85578a7e714167fffa0e31"},"schema_version":"1.0","source":{"id":"1807.10819","kind":"arxiv","version":1}},"canonical_sha256":"63adc4d6fc06b35dfea76008fb03618db99166d3e30a1654833f94b41bb4fb2f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63adc4d6fc06b35dfea76008fb03618db99166d3e30a1654833f94b41bb4fb2f","first_computed_at":"2026-05-18T00:09:37.312962Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:37.312962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KV8LGVekWyMWR20Dtjt4bGzJpnrZqU1fZZK+8wgBYlWF6fXREwzhgXq8wNh3N1WjwfwoDZP80D1X2ESt7rYOCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:37.313477Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.10819","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11ed7cd5bc2e5ed71d9e67b08f54686680d70050b07162a52b6448c8247cb4a1","sha256:33ab04b438739c94eef77dcf5bc2aee776e708cb96d5a8d0df1b35b14bbb3618"],"state_sha256":"699e3a5dd6517c9e03fd0efacdb6601f22d147c49d74e81f097acab2319cc267"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WYP6V6xJDvi+snkPthsRKrqZc3cP0eZAgxlWyG/KhGi0Y4VpFzQIwfntKWFOJCL/AMAOkno0yJ0P75n/kUSPDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:36:14.058778Z","bundle_sha256":"8761d21d584dbafb73acd4538c55556d4a4805b8b62e1b0ab7e2b3fb7c8efa25"}}