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CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation., , , , and . MIDL, volume 143 of Proceedings of Machine Learning Research, page 190-204. PMLR, (2021)CheXphoto: 10, 000+ Smartphone Photos and Synthetic Photographic Transformations of Chest X-rays for Benchmarking Deep Learning Robustness., , , , , , , , , and . CoRR, (2020)Know What You Don't Know: Unanswerable Questions for SQuAD., , and . ACL (2), page 784-789. Association for Computational Linguistics, (2018)RadGraph: Extracting Clinical Entities and Relations from Radiology Reports., , , , , , , , , and 2 other author(s). NeurIPS Datasets and Benchmarks, (2021)LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype., , , , , , , , , and 3 other author(s). ML4H@NeurIPS, volume 225 of Proceedings of Machine Learning Research, page 528-558. PMLR, (2023)CheXternal: generalization of deep learning models for chest X-ray interpretation to photos of chest X-rays and external clinical settings., , , , and . CHIL, page 125-132. ACM, (2021)CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays., , , , and . CoRR, (2021)Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models., , , , and . CoRR, (2023)Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation., , , and . CoRR, (2021)Human-machine partnership with artificial intelligence for chest radiograph diagnosis., , , , , , , , , and 9 other author(s). npj Digit. Medicine, (2019)