AIMC Topic: Deep Learning

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Shunt connection: An intelligent skipping of contiguous blocks for optimizing MobileNet-V2.

Neural networks : the official journal of the International Neural Network Society
Enabling deep neural networks for tight resource constraint environments like mobile phones and cameras is the current need. The existing availability in the form of optimized architectures like Squeeze Net, MobileNet etc., are devised to serve the p...

A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models shoul...

An image-based deep learning framework for individualizing radiotherapy dose.

The Lancet. Digital health
BACKGROUND: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to i...

Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer.

Health informatics journal
This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions ...

Riemannian Curvature of Deep Neural Networks.

IEEE transactions on neural networks and learning systems
We analyze deep neural networks using the theory of Riemannian geometry and curvature. The objective is to gain insight into how Riemannian geometry can characterize and predict the trained behavior of neural networks. We define a method for calculat...

TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

Medical image analysis
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase ima...

Deep learning in medical image analysis: A third eye for doctors.

Journal of stomatology, oral and maxillofacial surgery
AIM AND SCOPE: Artificial intelligence (AI) in medicine is a fast-growing field. The rise of deep learning algorithms, such as convolutional neural networks (CNNs), offers fascinating perspectives for the automation of medical image analysis. In this...

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.

Sensors (Basel, Switzerland)
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to...

Neural networks versus Logistic regression for 30 days all-cause readmission prediction.

Scientific reports
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the health...