AIMC Topic: Deep Learning

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Deep learning methods to predict amyotrophic lateral sclerosis disease progression.

Scientific reports
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target...

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks.

Scientific reports
As an effective approach to perceive environments, acoustic scene classification (ASC) has received considerable attention in the past few years. Generally, ASC is deemed a challenging task due to subtle differences between various classes of environ...

Deep learning for quality assessment of optical coherence tomography angiography images.

Scientific reports
Optical coherence tomography angiography (OCTA) is an emerging non-invasive technique for imaging the retinal vasculature. While there are many promising clinical applications for OCTA, determination of image quality remains a challenge. We developed...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Scientific reports
Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially hi...

Deep-learning seismology.

Science (New York, N.Y.)
Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pus...

Visualization deep learning model for automatic arrhythmias classification.

Physiological measurement
With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations a...

A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Physiological measurement
Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone...

NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.

Frontiers in immunology
Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing ne...

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning.

Computational and mathematical methods in medicine
The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that to...

A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction.

Disease markers
The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge repre...