AIMC Topic: Deep Learning

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Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

Journal of neural transmission (Vienna, Austria : 1996)
A wearable sensor system is available for monitoring of bradykinesia in patients with Parkinson's disease (PD), however, it remains unclear whether kinematic parameters would reflect clinical severity of PD, or would help clinical diagnosis of physic...

A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation.

IEEE transactions on visualization and computer graphics
In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visu...

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement.

Sensors (Basel, Switzerland)
BACKGROUND: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundari...

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BMC medical imaging
BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although...

Deep Learning-Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors.

JMIR mHealth and uHealth
BACKGROUND: Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative inf...

Evaluating Deep Learning models for predicting ALK-5 inhibition.

PloS one
Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the...

Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.

PloS one
BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investig...

Deep Learning Architecture to Assist With Steering a Powered Wheelchair.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for st...

A tree-based multiclassification of breast tumor histopathology images through deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for...

Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach.

Medical physics
PURPOSE: To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).