{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:L3B7EI54JPS2KNLDEF6X63RTNY","short_pith_number":"pith:L3B7EI54","schema_version":"1.0","canonical_sha256":"5ec3f223bc4be5a53563217d7f6e336e0d165d87dba4e8bfdbbd1e644ec05d27","source":{"kind":"arxiv","id":"1710.05719","version":2},"attestation_state":"computed","paper":{"title":"Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Aryan Mobiny, Hien Van Nguyen, Supratik Moulik","submitted_at":"2017-10-11T14:54:04Z","abstract_excerpt":"In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans. Using less than 10,000 training examples, our deep networks perform two times better than a standard radiology software. Visualization of the networks' neurons reveals semantically meaningful features that are consistent with the clinical knowledge and radiologists' perception. Our paper also proposes a novel framework for rapidly adapting deep networks to the radiologists' feedback, or change in the data due to the shift in sensor's resolution or patient pop"},"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":"1710.05719","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-11T14:54:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a5cb5a0c9bc3998cf2034f8af4553a0acfa51465f0c96130048de8bc7dc20fd9","abstract_canon_sha256":"25647c42e4c0e5590875ede53830b2b522d376ea60d64c672ca84e0031c51d25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:20.768285Z","signature_b64":"UBDtguqC1rtSY1V8WfyLp7rzY6uhTKI++jXpE5crchQC5o9255O9P9aHs+/J8uIUtDivJUYOZZmCTxQ/ExInCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ec3f223bc4be5a53563217d7f6e336e0d165d87dba4e8bfdbbd1e644ec05d27","last_reissued_at":"2026-05-18T00:06:20.767864Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:20.767864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Aryan Mobiny, Hien Van Nguyen, Supratik Moulik","submitted_at":"2017-10-11T14:54:04Z","abstract_excerpt":"In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans. Using less than 10,000 training examples, our deep networks perform two times better than a standard radiology software. Visualization of the networks' neurons reveals semantically meaningful features that are consistent with the clinical knowledge and radiologists' perception. Our paper also proposes a novel framework for rapidly adapting deep networks to the radiologists' feedback, or change in the data due to the shift in sensor's resolution or patient pop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.05719","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":"1710.05719","created_at":"2026-05-18T00:06:20.767927+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.05719v2","created_at":"2026-05-18T00:06:20.767927+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.05719","created_at":"2026-05-18T00:06:20.767927+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3B7EI54JPS2","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3B7EI54JPS2KNLD","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3B7EI54","created_at":"2026-05-18T12:31:28.150371+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/L3B7EI54JPS2KNLDEF6X63RTNY","json":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY.json","graph_json":"https://pith.science/api/pith-number/L3B7EI54JPS2KNLDEF6X63RTNY/graph.json","events_json":"https://pith.science/api/pith-number/L3B7EI54JPS2KNLDEF6X63RTNY/events.json","paper":"https://pith.science/paper/L3B7EI54"},"agent_actions":{"view_html":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY","download_json":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY.json","view_paper":"https://pith.science/paper/L3B7EI54","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.05719&json=true","fetch_graph":"https://pith.science/api/pith-number/L3B7EI54JPS2KNLDEF6X63RTNY/graph.json","fetch_events":"https://pith.science/api/pith-number/L3B7EI54JPS2KNLDEF6X63RTNY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY/action/storage_attestation","attest_author":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY/action/author_attestation","sign_citation":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY/action/citation_signature","submit_replication":"https://pith.science/pith/L3B7EI54JPS2KNLDEF6X63RTNY/action/replication_record"}},"created_at":"2026-05-18T00:06:20.767927+00:00","updated_at":"2026-05-18T00:06:20.767927+00:00"}