AIMC Topic: Deep Learning

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Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model.

European radiology
OBJECTIVES: Automatic bone lesions detection and classifications present a critical challenge and are essential to support radiologists in making an accurate diagnosis of bone lesions. In this paper, we aimed to develop a novel deep learning model ca...

DLA-H: A Deep Learning Accelerator for Histopathologic Image Classification.

Journal of digital imaging
It is more than a decade since machine learning and especially its leading subtype deep learning have become one of the most interesting topics in almost all areas of science and industry. In numerous contexts, at least one of the applications of dee...

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.

Journal of digital imaging
Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-lear...

VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately predicting the progression trend of MCI is critical to the early prevention and treatment of AD. Brain...

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image reconstruction from low-dose tomographic data is an active research field, recently revolutionized by the advent of deep learning. In fact, deep learning typically yields superior results than classical optimization approaches, but unst...

Off the deep end: What can deep learning do for the gene expression field?

The Journal of biological chemistry
After a COVID-related hiatus, the fifth biennial symposium on Evolution and Core Processes in Gene Regulation met at the Stowers Institute in Kansas City, Missouri July 21 to 24, 2022. This symposium, sponsored by the American Society for Biochemistr...

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nucleosides, nucleotides & nucleic acids
Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritio...

End-to-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery.

IEEE transactions on neural networks and learning systems
Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hiera...

A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing.

IEEE transactions on neural networks and learning systems
Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, and interference avoidance. Recently, deep learnin...

Learned Gradient Compression for Distributed Deep Learning.

IEEE transactions on neural networks and learning systems
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed training, where a model is replicated into several computational nodes th...