David A. Mong

TL;DR: CheXpert as discussed by the authors is a large dataset of chest radiographs of 65,240 patients annotated by 3 board-certified radiologists with 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation.

TL;DR: CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs, achieved radiologist-level performance on 11 pathologies and did not achieve radiologists' level performance on 3 pathologies.

TL;DR: CheXpert as discussed by the authors is a large dataset of chest radiographs of 65,240 patients annotated by 3 board-certified radiologists with 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation and different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs.

TL;DR: Investigation of cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost suggests feasibility of broader usage of neural network models in automated classification of multi-Institutional imaging text reports.

TL;DR: A novel graph‐based technique for compiling the best 4D image volume representing the thorax over one respiratory cycle from slice images acquired during unencumbered natural tidal‐breathing of pediatric TIS patients and it is believed that the method can be routinely used for thoracic 4D imaging.