Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …

Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Physics-informed neural networks (PINNs) have recently emerged as an alternative way of
numerically solving partial differential equations (PDEs) without the need of building …

PowerPoint 演示文稿 Page 1 Uncertainty in Deep Learning Yarin Gal 2018.7.29 Page 2 Page
3 Different Uncertainties Two main types of uncertainty, often confused by practitioners, but …

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver
registration for image-pairs works by patch-wise prediction of a deformation model based …

Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …