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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)CheXphoto: 10, 000+ Smartphone Photos and Synthetic Photographic Transformations of Chest X-rays for Benchmarking Deep Learning Robustness., , , , , , , , , and . CoRR, (2020)Human-machine partnership with artificial intelligence for chest radiograph diagnosis., , , , , , , , , and 9 other author(s). npj Digit. Medicine, (2019)CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV., , , , , , , , , and 4 other author(s). npj Digit. Medicine, (2020)Author Correction: PENet - a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging., , , , , , , , , and 7 other author(s). npj Digit. Medicine, (2020)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)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)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)