Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …

In recent years, there has been enormous interest in applying artificial intelligence (AI) to
radiology. Although some of this interest may have been driven by exaggerated …

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …

Surgical tool detection is attracting increasing attention from the medical image analysis
community. The goal generally is not to precisely locate tools in images, but rather to …

The human cerebellum plays an essential role in motor control, is involved in cognitive
function (ie, attention, working memory, and language), and helps to regulate emotional …

Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …

Transfer learning is a widely used strategy in medical image analysis. Instead of only
training a network with a limited amount of data from the target task of interest, we can first …

The article presents an overview of the status quo in benchmarking in classification and
nonlinear regression. It outlines guidelines for a comparative analysis in machine learning …

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have
demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung …

Medical imaging is an important research field with many opportunities for improving
patients' health. However, there are a number of challenges that are slowing down the …