AIMC Topic: Deep Learning

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Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondes...

Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality.

Journal of computer assisted tomography
Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduct...

Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds.

The American journal of gastroenterology
OBJECTIVES: Reliable in situ diagnosis of diminutive (≤5 mm) colorectal polyps could allow for "resect and discard" and "diagnose and leave" strategies, resulting in $1 billion cost savings per year in the United States alone. Current methodologies h...

Applied Deep Learning in Plastic Surgery: Classifying Rhinoplasty With a Mobile App.

The Journal of craniofacial surgery
BACKGROUND: Advances in deep learning (DL) have been transformative in computer vision and natural language processing, as well as in healthcare. The authors present a novel application of DL to plastic surgery. Here, the authors describe and demonst...

A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance Images: A Preliminary Machine Learning Study.

Turkish neurosurgery
AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans.

Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically...

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evalu...