M11-093L: Development of a Highly Efficient and User-Friendly Software System for Carotid Intima-Media Thickness- Researchers at Arizona State University have developed a highly user-friendly system for semiautomatic CIMT image interpretation. Their contribution is the application of active contour models (snake models) with hard constraints, leading...

­ As machine learning grows and advances, contrastive representation learning continues to emerge as the state-of-the-art technique in computer vision. Contrastive representation learning, however, has major limitations that make it problematic for 3D medical imaging, such as requiring extensive mini-batch sizes, special network design, or memory...

­As machine learning grows and advances, contrastive representation learning continues to emerge as the state-of-the-art technique in computer vision. Contrastive representation learning, however, has major limitations that make it problematic for 3D medical imaging, such as requiring extensive mini-batch sizes, special network design, or memory...

­ Computer-aided diagnosis (CAD) systems are invaluable in helping physicians better diagnose and treat diseases by using Artificial Intelligence (AI) to interpret medical images. Motivated by the early success of CAD systems, particularly those developed using deep learning algorithms, there is an intense interest in adopting them for applications...

Image analysis techniques are invaluable in helping physicians better diagnose and treat diseases and expand the utility of medical imaging. Self-supervised learning, in particular, is one of the most practical paradigms in deep learning for medical image analysis. Self-supervised learning methods are characterized by training deep models directly from...

Image analysis techniques are invaluable in helping physicians better diagnose and treat diseases and expand the utility of medical imaging. Self-supervised learning, in particular, is one of the most practical paradigms in deep learning for medical image analysis. Self-supervised learning methods are characterized by training deep models directly from...

Image analysis techniques are becoming invaluable in the medical field, as they have been shown to help physicians better diagnose and treat diseases and expand the utility of medical imaging. Transfer learning, in particular, is one of the most practical paradigms in deep learning for medical image analysis. In conventional transfer learning, source...

Image analysis techniques are becoming invaluable in the medical field, as they have been shown to help physicians better diagnose and treat diseases and expand the utility of medical imaging. Transfer learning, in particular, is one of the most practical paradigms in deep learning for medical image analysis. In conventional transfer learning, source...

Fully convolutional networks (FCN) and variants of U-Net are the state-of-the-art models for medical image segmentation. However, these models have limitations, namely 1. their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models and 2. their skip connections impose a restrictive fusion scheme,...

Deep convolutional neural networks (CNN) are useful in a variety of applications ranging from computer vision to signal processing. There is increasing interest in applying CNN to biomedical image analysis, but success is impeded by the lack of large annotated datasets in biomedical imaging. Expert annotation is tedious, time consuming and expensive...