Background Perovskites have widely been considered as promising next-generation materials for future display and solid-state lighting applications. Most current perovskite LEDs are produced using solution processing, and over 20% external quantum efficiencies (EQE) in LEDs have been achieved in recent studies. Vacuum-based deposition has also attracted...

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...

­Medical Imaging is an important tool utilized for diagnosing, monitoring and treating medical conditions. In medical imaging, anatomical or pathological structures may be difficult to distinguish, thus medical image segmentation is utilized to make those structures clearer. Manual segmentation approaches are tedious, and expensive. Deep convolutional...

­ 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 invaluable in helping physicians better diagnose and treat diseases and expand the utility of medical imaging. Two common image analysis techniques, convolutional neural networks (CNN) and bags of visual words (BoVW) are often thought of as competing methods, however, they could have complementary strengths. CNNs are great...

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...