Artificial intelligence (AI) plays a major role in addressing novel coronavirus 2019 (COVID-19)-related issues and is also used in computer-aided synthesis planning (CASP). AI, including machine learning, is used by artificial neural networks such as...
Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular ...
This research specifically addresses the prediction of dairy product demand (DPD). Since dairy products have a short consumption period, it is important to have accurate information about their future demand. The main contribution of this research is...
IMPORTANCE: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring p...
Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by m...
Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19...
Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive un...
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodat...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal representation for action recognition in videos. However, most traditional action recognition algorithms do not employ the attention mechanism to focus on essent...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Deep neural networks (DNNs), characterized by sophisticated architectures capable of learning a hierarchy of feature representations, have achieved remarkable successes in various applications. Learning DNN's parameters is a crucial but challenging t...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.