AIMC Topic: Deep Learning

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Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning.

Magnetic resonance imaging
BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive l...

Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes.

Artificial intelligence in medicine
The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complications. The trajectories of patients in real-world settings are highly variable, making longitudinal data...

An interpretable deep learning model for classifying adaptor protein complexes from sequence information.

Methods (San Diego, Calif.)
Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex tech...

Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation.

Proceedings of the National Academy of Sciences of the United States of America
Collagen is the most abundant structural protein in humans, providing crucial mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials are, therefore, used for tissue repair and regeneration. Utilizing col...

A Deep Learning Approach for the Estimation of Glomerular Filtration Rate.

IEEE transactions on nanobioscience
An accurate estimation of glomerular filtration rate (GFR) is clinically crucial for kidney disease diagnosis and predicting the prognosis of chronic kidney disease (CKD). Machine learning methodologies such as deep neural networks provide a potentia...

Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging.

Sensors (Basel, Switzerland)
Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixe...

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer.

International journal of molecular sciences
BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations ar...

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning.

Nature communications
Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinical trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by ...

Deep learning techniques for detecting and recognizing face masks: A survey.

Frontiers in public health
The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccin...

Strategies for Ideological and Political Education in Colleges and Universities Based on Deep Learning.

Computational intelligence and neuroscience
Ideological and political education in colleges and universities is routinely burdened with the job of building morality and cultivating people, which is related to the cultivation of college students' ideals and beliefs, spiritual pursuits, and poli...