AIMC Topic: Deep Learning

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Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility.

Neurosurgery
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity of data required and lack of model interpretability represent substantial barriers to the validity a...

Transfer Learning of the ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning.

Current pharmaceutical design
OBJECTIVE: The aim of the study was to verify the ability of the deep learning model to identify five subtypes and normal images in non-contrast enhancement CT of intracranial hemorrhage.

A coronary artery CTA segmentation approach based on deep learning.

Journal of X-ray science and technology
Presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. Detection of plaque and coronary artery segmentation have become the first choice in detecting coronary artery disease. The purpose of this study is to inv...

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of pr...

Interpretable deep learning prediction of 3d assessment of cardiac function.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
As deep learning plays an increasing role in making medical decisions, explainability is playing an increasing role in satisfying regulatory requirements and facilitating trust and transparency in deep learning approaches. In cardiac imaging, the tas...

Session introduction: AI-driven Advances in Modeling of Protein Structure.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The last few years mark dramatic improvements in modeling of protein structure. Progress was initially due to breakthroughs in residue-residue contact prediction, first with global statistical models and later with deep learning. These advancements w...

The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia.

The British journal of radiology
OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcrip...

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients.

Journal of X-ray science and technology
BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved.

Introduction to Deep Learning in Clinical Neuroscience.

Acta neurochirurgica. Supplement
The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyz...