AJR. American journal of roentgenology
Apr 29, 2020
The purpose of this study was to assess, by analyzing features of the primary tumor with F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung can...
International journal of computer assisted radiology and surgery
Apr 29, 2020
PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomose...
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated descr...
OBJECTIVE: To develop and test a deep learning algorithm to automatically detect cortical tubers in magnetic resonance imaging (MRI), to explore the utility of deep learning in rare disorders with limited data, and to generate an open-access deep lea...
OBJECTIVES:  The current study sought to evaluate whether nursing narratives can be used to predict postoperative length of hospital stay (LOS) following curative surgery for ovarian cancer.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Apr 28, 2020
INTRODUCTION: The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction o...
PURPOSE: Patient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for i...
Fine-grained categorization is one of the most challenging problems in machine vision. Recently, the presented methods have been based on convolutional neural networks, increasing the accuracy of classification very significantly. Inspired by these m...
Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods requ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.