{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WQKVLL2CBAYCXOIBSUX2DP6RBR","short_pith_number":"pith:WQKVLL2C","canonical_record":{"source":{"id":"1907.07816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-17T23:51:24Z","cross_cats_sorted":[],"title_canon_sha256":"7dbe8f3b4d2c31135c4feea755f2382e1860dcdd316542b492360823bf2a05e7","abstract_canon_sha256":"a4f04adbab5256452841f9534cda1cd0328bedeae6ee96fe0e162191ac787723"},"schema_version":"1.0"},"canonical_sha256":"b41555af4208302bb901952fa1bfd10c5adffd1374666f612a9d819ea49d7af7","source":{"kind":"arxiv","id":"1907.07816","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.07816","created_at":"2026-05-17T23:40:16Z"},{"alias_kind":"arxiv_version","alias_value":"1907.07816v1","created_at":"2026-05-17T23:40:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07816","created_at":"2026-05-17T23:40:16Z"},{"alias_kind":"pith_short_12","alias_value":"WQKVLL2CBAYC","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WQKVLL2CBAYCXOIB","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WQKVLL2C","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WQKVLL2CBAYCXOIBSUX2DP6RBR","target":"record","payload":{"canonical_record":{"source":{"id":"1907.07816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-17T23:51:24Z","cross_cats_sorted":[],"title_canon_sha256":"7dbe8f3b4d2c31135c4feea755f2382e1860dcdd316542b492360823bf2a05e7","abstract_canon_sha256":"a4f04adbab5256452841f9534cda1cd0328bedeae6ee96fe0e162191ac787723"},"schema_version":"1.0"},"canonical_sha256":"b41555af4208302bb901952fa1bfd10c5adffd1374666f612a9d819ea49d7af7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:16.194978Z","signature_b64":"kUBzbgLNJfV9ODj7zaYv4ehPl5YTXsSod/kBBno4pohMx0XKGleCErbVTJF6JRv04nxo9WkzLwNvIjs2pwefAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b41555af4208302bb901952fa1bfd10c5adffd1374666f612a9d819ea49d7af7","last_reissued_at":"2026-05-17T23:40:16.194189Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:16.194189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.07816","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":"2026-05-17T23:40:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZyMCy19hg6/50sCtQ/1dBkf+T3QpQrMdYgVSJoqoxwdzBPgDFwGj5P/YgGNg4EBWCqDMa6u57MZga0w95xgeBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:14:52.191364Z"},"content_sha256":"13ab4a674077293c9e0e559d650bb6c4ab0cea260fd038f9b91cac0040e56321","schema_version":"1.0","event_id":"sha256:13ab4a674077293c9e0e559d650bb6c4ab0cea260fd038f9b91cac0040e56321"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WQKVLL2CBAYCXOIBSUX2DP6RBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Task Design to Meta-Train Medical Image Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cuong Nguyen, Farbod Motlagh, Gabriel Maicas, Gustavo Carneiro, Jacinto C. Nascimento","submitted_at":"2019-07-17T23:51:24Z","abstract_excerpt":"Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with small training sets). However, the effectiveness of meta-training relies on the availability of a reasonable number of hand-designed classification tasks, which are costly to obtain, and consequently rarely available. In this paper, we propose a new method to unsupervisedly design a large number of classification tasks to meta-train medical image classifiers. We evaluate our method on a breast dynamically contrast enhanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07816","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":"2026-05-17T23:40:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rruNuPV6Y9SwaM1nea7plAfriFcDRzmJ3Qj6LD/ENzcIst0QdUp6AR6Qw3HX/KLI/v5TORyjozbplBq+pqkTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:14:52.192041Z"},"content_sha256":"8892575a18d51aced0259109c18ab1955440a6434987b27243783b1ddcd28d86","schema_version":"1.0","event_id":"sha256:8892575a18d51aced0259109c18ab1955440a6434987b27243783b1ddcd28d86"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR/bundle.json","state_url":"https://pith.science/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR/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":"2026-05-27T03:14:52Z","links":{"resolver":"https://pith.science/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR","bundle":"https://pith.science/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR/bundle.json","state":"https://pith.science/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WQKVLL2CBAYCXOIBSUX2DP6RBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WQKVLL2CBAYCXOIBSUX2DP6RBR","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":"a4f04adbab5256452841f9534cda1cd0328bedeae6ee96fe0e162191ac787723","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-17T23:51:24Z","title_canon_sha256":"7dbe8f3b4d2c31135c4feea755f2382e1860dcdd316542b492360823bf2a05e7"},"schema_version":"1.0","source":{"id":"1907.07816","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.07816","created_at":"2026-05-17T23:40:16Z"},{"alias_kind":"arxiv_version","alias_value":"1907.07816v1","created_at":"2026-05-17T23:40:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07816","created_at":"2026-05-17T23:40:16Z"},{"alias_kind":"pith_short_12","alias_value":"WQKVLL2CBAYC","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WQKVLL2CBAYCXOIB","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WQKVLL2C","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:8892575a18d51aced0259109c18ab1955440a6434987b27243783b1ddcd28d86","target":"graph","created_at":"2026-05-17T23:40:16Z","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":"Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with small training sets). However, the effectiveness of meta-training relies on the availability of a reasonable number of hand-designed classification tasks, which are costly to obtain, and consequently rarely available. In this paper, we propose a new method to unsupervisedly design a large number of classification tasks to meta-train medical image classifiers. We evaluate our method on a breast dynamically contrast enhanc","authors_text":"Cuong Nguyen, Farbod Motlagh, Gabriel Maicas, Gustavo Carneiro, Jacinto C. Nascimento","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-17T23:51:24Z","title":"Unsupervised Task Design to Meta-Train Medical Image Classifiers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07816","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":"sha256:13ab4a674077293c9e0e559d650bb6c4ab0cea260fd038f9b91cac0040e56321","target":"record","created_at":"2026-05-17T23:40:16Z","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":"a4f04adbab5256452841f9534cda1cd0328bedeae6ee96fe0e162191ac787723","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-17T23:51:24Z","title_canon_sha256":"7dbe8f3b4d2c31135c4feea755f2382e1860dcdd316542b492360823bf2a05e7"},"schema_version":"1.0","source":{"id":"1907.07816","kind":"arxiv","version":1}},"canonical_sha256":"b41555af4208302bb901952fa1bfd10c5adffd1374666f612a9d819ea49d7af7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b41555af4208302bb901952fa1bfd10c5adffd1374666f612a9d819ea49d7af7","first_computed_at":"2026-05-17T23:40:16.194189Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:16.194189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kUBzbgLNJfV9ODj7zaYv4ehPl5YTXsSod/kBBno4pohMx0XKGleCErbVTJF6JRv04nxo9WkzLwNvIjs2pwefAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:16.194978Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.07816","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:13ab4a674077293c9e0e559d650bb6c4ab0cea260fd038f9b91cac0040e56321","sha256:8892575a18d51aced0259109c18ab1955440a6434987b27243783b1ddcd28d86"],"state_sha256":"9830cc7027d97525e39dab07d69b2419bc36a41982ce17dfeb3c06e25d93cbbc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qYA71CHDSIWLpNLOQZx3dSAKMYOqIctyhM+p4ZQusOHNhLgq+kzkYR8ikGB53iJ6+uoeSYMtqWx/uqiEuQbsDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T03:14:52.196706Z","bundle_sha256":"4453173f81f925fd3fd82fd9ccbdfec24489e50c630d9a681d88664f15554d8f"}}