{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZTXXCAQSL4MFCDX2DORKSZ3PB2","short_pith_number":"pith:ZTXXCAQS","schema_version":"1.0","canonical_sha256":"ccef7102125f18510efa1ba2a9676f0ead7a30119f5b0c7317578271f324974b","source":{"kind":"arxiv","id":"1711.05225","version":3},"attestation_state":"computed","paper":{"title":"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Aarti Bagul, Andrew Y. Ng, Brandon Yang, Curtis Langlotz, Daisy Ding, Hershel Mehta, Jeremy Irvin, Katie Shpanskaya, Kaylie Zhu, Matthew P. Lungren, Pranav Rajpurkar, Tony Duan","submitted_at":"2017-11-14T17:58:50Z","abstract_excerpt":"We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX"},"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":"1711.05225","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-14T17:58:50Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b7b62c0f5c0946ee4ecbdca330ba69cf89dbd78e065bc048e969c49b75ed77f2","abstract_canon_sha256":"a05096679ddd6e16e51e81a3c35a3f12c716735df33f46838e29a037de7870d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:17.985096Z","signature_b64":"SLXnkI/sO1oaq3dfXHXrZVytw18XLn09gmEuNIo6Gji96haPF0y6NV5XpHvPA0C6nmNKn0FwUnsTC6iu2QH5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccef7102125f18510efa1ba2a9676f0ead7a30119f5b0c7317578271f324974b","last_reissued_at":"2026-05-18T00:27:17.984543Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:17.984543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Aarti Bagul, Andrew Y. Ng, Brandon Yang, Curtis Langlotz, Daisy Ding, Hershel Mehta, Jeremy Irvin, Katie Shpanskaya, Kaylie Zhu, Matthew P. Lungren, Pranav Rajpurkar, Tony Duan","submitted_at":"2017-11-14T17:58:50Z","abstract_excerpt":"We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05225","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":""},"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":"1711.05225","created_at":"2026-05-18T00:27:17.984622+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05225v3","created_at":"2026-05-18T00:27:17.984622+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05225","created_at":"2026-05-18T00:27:17.984622+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZTXXCAQSL4MF","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZTXXCAQSL4MFCDX2","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZTXXCAQS","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":21,"internal_anchor_count":13,"sample":[{"citing_arxiv_id":"1906.09336","citing_title":"Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"1906.11282","citing_title":"Developing an App to interpret Chest X-rays to support the diagnosis of respiratory pathology with Artificial Intelligence","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"1907.05671","citing_title":"Justifying Diagnosis Decisions by Deep Neural Networks","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2006.05332","citing_title":"Advance Warning Methodologies for COVID-19 using Chest X-Ray Images","ref_index":32,"is_internal_anchor":true},{"citing_arxiv_id":"2005.04014","citing_title":"Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2504.12654","citing_title":"The Paradox of Professional Input: How Expert Collaboration with AI Systems Shapes Their Future Value","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2603.29167","citing_title":"JDCNet: Confidence-Gated Privileged-Modality Distillation for Cost-Preserving X-ray Inference","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17729","citing_title":"Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19201","citing_title":"On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16476","citing_title":"Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16993","citing_title":"Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2303.13375","citing_title":"Capabilities of GPT-4 on Medical Challenge Problems","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2603.17765","citing_title":"Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10142","citing_title":"Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10546","citing_title":"Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10054","citing_title":"Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02292","citing_title":"Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00448","citing_title":"Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12305","citing_title":"CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07340","citing_title":"A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02328","citing_title":"Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2","json":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2.json","graph_json":"https://pith.science/api/pith-number/ZTXXCAQSL4MFCDX2DORKSZ3PB2/graph.json","events_json":"https://pith.science/api/pith-number/ZTXXCAQSL4MFCDX2DORKSZ3PB2/events.json","paper":"https://pith.science/paper/ZTXXCAQS"},"agent_actions":{"view_html":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2","download_json":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2.json","view_paper":"https://pith.science/paper/ZTXXCAQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05225&json=true","fetch_graph":"https://pith.science/api/pith-number/ZTXXCAQSL4MFCDX2DORKSZ3PB2/graph.json","fetch_events":"https://pith.science/api/pith-number/ZTXXCAQSL4MFCDX2DORKSZ3PB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2/action/storage_attestation","attest_author":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2/action/author_attestation","sign_citation":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2/action/citation_signature","submit_replication":"https://pith.science/pith/ZTXXCAQSL4MFCDX2DORKSZ3PB2/action/replication_record"}},"created_at":"2026-05-18T00:27:17.984622+00:00","updated_at":"2026-05-18T00:27:17.984622+00:00"}