{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IP2NDRCL4SSDWQZKD5HNSSSXNZ","short_pith_number":"pith:IP2NDRCL","schema_version":"1.0","canonical_sha256":"43f4d1c44be4a43b432a1f4ed94a576e4eeb7872210ffbc5c163635fa3656b92","source":{"kind":"arxiv","id":"1809.09287","version":2},"attestation_state":"computed","paper":{"title":"MedAL: Deep Active Learning Sampling Method for Medical Image Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adri\\'an Galdr\\'an, Asim Smailagic, Devesh Walawalkar, Hae Young Noh, Jonathon Fagert, Kartik Khandelwal, Mostafa Mirshekari, Pedro Costa, Susu Xu","submitted_at":"2018-09-25T02:30:24Z","abstract_excerpt":"Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance.In this work, we propose a novel sampling method that queries the unlabeled examples t"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1809.09287","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-09-25T02:30:24Z","cross_cats_sorted":[],"title_canon_sha256":"62b2f79bc72f0f5aec364ea47b869df8b53e125ded61ae0cbcd0998ca6aad16b","abstract_canon_sha256":"a724f511755f70ac161e54db20bb80e21829f7bc8100ec6d0a5c8796c52976a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:29.113702Z","signature_b64":"sZMRIvsNDP1B3o9xjI/E6uImsp44jMXUpU5T+dvg3PfSpuNV5Vn0pgicQbb4kqCBIfTfnrWslvsnKoI9iZdADg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43f4d1c44be4a43b432a1f4ed94a576e4eeb7872210ffbc5c163635fa3656b92","last_reissued_at":"2026-05-18T00:04:29.113228Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:29.113228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedAL: Deep Active Learning Sampling Method for Medical Image Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adri\\'an Galdr\\'an, Asim Smailagic, Devesh Walawalkar, Hae Young Noh, Jonathon Fagert, Kartik Khandelwal, Mostafa Mirshekari, Pedro Costa, Susu Xu","submitted_at":"2018-09-25T02:30:24Z","abstract_excerpt":"Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance.In this work, we propose a novel sampling method that queries the unlabeled examples t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.09287","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1809.09287","created_at":"2026-05-18T00:04:29.113292+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.09287v2","created_at":"2026-05-18T00:04:29.113292+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.09287","created_at":"2026-05-18T00:04:29.113292+00:00"},{"alias_kind":"pith_short_12","alias_value":"IP2NDRCL4SSD","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IP2NDRCL4SSDWQZK","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IP2NDRCL","created_at":"2026-05-18T12:32:31.084164+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ","json":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ.json","graph_json":"https://pith.science/api/pith-number/IP2NDRCL4SSDWQZKD5HNSSSXNZ/graph.json","events_json":"https://pith.science/api/pith-number/IP2NDRCL4SSDWQZKD5HNSSSXNZ/events.json","paper":"https://pith.science/paper/IP2NDRCL"},"agent_actions":{"view_html":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ","download_json":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ.json","view_paper":"https://pith.science/paper/IP2NDRCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.09287&json=true","fetch_graph":"https://pith.science/api/pith-number/IP2NDRCL4SSDWQZKD5HNSSSXNZ/graph.json","fetch_events":"https://pith.science/api/pith-number/IP2NDRCL4SSDWQZKD5HNSSSXNZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ/action/storage_attestation","attest_author":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ/action/author_attestation","sign_citation":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ/action/citation_signature","submit_replication":"https://pith.science/pith/IP2NDRCL4SSDWQZKD5HNSSSXNZ/action/replication_record"}},"created_at":"2026-05-18T00:04:29.113292+00:00","updated_at":"2026-05-18T00:04:29.113292+00:00"}