Bhavik N. Patel

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: A deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams is developed and the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation is supported.

TL;DR: Advanced imaging modalities such as diffusion tensor imaging and perfusion MR imaging, MR spectroscopy, and positron emission tomography can be useful in differentiating between recurrent tumor and radiation necrosis, with restricted diffusion and an elevated relative cerebral blood volume being seen much more frequently in recurrent tumor than in Radiation necrosis.