Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macr...
BACKGROUND: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedic...
OBJECTIVE: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression mode...
Computational and mathematical methods in medicine
Jul 21, 2019
The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic ...
PURPOSE: To effectively grade hepatocellular carcinoma (HCC) based on deep features derived from diffusion weighted images (DWI) with multiple b-values using convolutional neural networks (CNN).
Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign - depending upon the severity of the infection and the current stage. The conventional methods require a detailed physical inspection by...
IEEE transactions on neural networks and learning systems
Jul 19, 2019
With the increasing popularity of social media and smart devices, the face as one of the key biometrics becomes vital for person identification. Among those face recognition algorithms, video-based face recognition methods could make use of both temp...
IEEE transactions on neural networks and learning systems
Jul 19, 2019
Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challengi...
The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, the existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional ne...
Grading spondylolisthesis into several stages from MRI images is challenging because detecting critical vertebrae and locating landmarks in images of different characteristics is difficult. We propose Faster Adversarial Recognition (FAR) network to a...
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