Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

Most of the existing chest X-ray datasets include labels from a list of findings without
specifying their locations on the radiographs. This limits the development of machine …

Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully
automated deep-learning pipeline for the standardization of chest X-ray images, for the …

Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …

Abstract Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural
network by maximizing the AUC score of the model on a dataset. Most previous works of …

The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Chest radiographs are among the most frequently acquired images in radiology and are
often the subject of computer vision research. However, most of the models used to classify …

We consider the problem of abnormality localization for clinical applications. While deep
learning has driven much recent progress in medical imaging, many clinical challenges are …