AIMC Topic: Deep Learning

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μ-law SGAN for generating spectra with more details in speech enhancement.

Neural networks : the official journal of the International Neural Network Society
The goal of monaural speech enhancement is to separate clean speech from noisy speech. Recently, many studies have employed generative adversarial networks (GAN) to deal with monaural speech enhancement tasks. When using generative adversarial networ...

Impact of the reperfusion status for predicting the final stroke infarct using deep learning.

NeuroImage. Clinical
BACKGROUND: Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and...

Deep Learning-based Recurrence Prediction in Patients with Non-muscle-invasive Bladder Cancer.

European urology focus
BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrence of the disease, which is difficult to predict.

Integrated analysis of machine learning and deep learning in chili pest and disease identification.

Journal of the science of food and agriculture
BACKGROUND: Chili is one of the most important and high-value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitor...

Applications of artificial intelligence in drug development using real-world data.

Drug discovery today
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. M...

Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks.

World neurosurgery
OBJECTIVE: Chronic subdural hematomas (cSDHs) are an increasingly prevalent neurologic disease that often requires surgical intervention to alleviate compression of the brain. Management of cSDHs relies heavily on computed tomography (CT) imaging, an...

Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability.

Computers in biology and medicine
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and...

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination.

Sensors (Basel, Switzerland)
We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the pro...

Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility.

International journal of environmental research and public health
This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, dep...