Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19)
has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (…

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline
patient diagnosis and management has become more pressing than ever. As one of the main …

This paper discusses how distribution matching losses, such as those used in CycleGAN,
when used to synthesize medical images can lead to mis-diagnosis of medical conditions. It …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

This large scale study focuses on quantifying what X-rays diagnostic prediction tasks
generalize well across multiple different datasets. We present evidence that the issue of …

… Joseph Paul Cohen 1 2 Paul Morrison 3 Lan Dao 4 … Correspondence to: Joseph Paul
Cohen <joseph@josephpcohen.com>. … (2017), which could predict pneumonia, as well …

Counting objects in digital images is a process that should be replaced by machines. This
tedious task is time consuming and prone to errors due to fatigue of human annotators. The …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most …

Poor generalization is one symptom of models that learn to predict target variables using
spuriously-correlated image features present only in the training distribution instead of the true …

TorchXRayVision is an open source software library for working with chest X-ray datasets and
deep learning models. It provides a common interface and common pre-processing chain …