AIMC Topic: Deep Learning

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Deep learning for screening of interstitial lung disease patterns in high-resolution CT images.

Clinical radiology Feb 16, 2020
AIM: To develop a screening tool for the detection of interstitial lung disease (ILD) patterns using a deep-learning method.
Datasets as Topic Humans Deep Learning Radiographic Image Interpretation, Computer-Assisted Lung Diseases, Interstitial Tomography, X-Ray Computed Sensitivity and Specificity
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Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning.

European radiology Feb 16, 2020
OBJECTIVES: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).
Aged Adult Sensitivity and Specificity Female Reproducibility of Results Acute Disease Pulmonary Embolism Heart Ventricles Male Middle Aged Deep Learning Humans Retrospective Studies Computed Tomography Angiography Ventricular Function, Right
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Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

Circulation. Arrhythmia and electrophysiology Feb 16, 2020
BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1)...
Follow-Up Studies Heart Ventricles Ethnicity Racial Groups Middle Aged Deep Learning Electrocardiography Male Ventricular Function, Left Humans Artificial Intelligence Retrospective Studies Female
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Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.

Internal and emergency medicine Feb 15, 2020
Pilot Projects Length of Stay Natural Language Processing Humans Hospitalization Deep Learning
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A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.

Ophthalmology Feb 15, 2020
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.
Geographic Atrophy Male Prospective Studies Retina Humans Deep Learning Female Fluorescein Angiography Follow-Up Studies Forecasting Disease Progression Aged Fundus Oculi Severity of Illness Index
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DENSE-INception U-net for medical image segmentation.

Computer methods and programs in biomedicine Feb 15, 2020
BACKGROUND AND OBJECTIVE: Convolutional neural networks (CNNs) play an important role in the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture is one of the most famous fully convolutional network architectures for...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Tomography, X-Ray Computed Magnetic Resonance Imaging Brain Neoplasms Deep Learning
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Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.

Computer methods and programs in biomedicine Feb 15, 2020
BACKGROUND AND OBJECTIVE: Performing patient-specific, pre-operative cochlea CT-based measurements could be helpful to positively affect the outcome of cochlear surgery in terms of intracochlear trauma and loss of residual hearing. Therefore, we prop...
Image Processing, Computer-Assisted Cochlear Implantation Cochlea Algorithms Tomography, X-Ray Computed Deep Learning Humans Neural Networks, Computer
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Towards deep learning for connectome mapping: A block decomposition framework.

NeuroImage Feb 15, 2020
We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated i...
Humans Brain Deep Learning Models, Neurological Connectome Diffusion Magnetic Resonance Imaging
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Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

Clinical neuroradiology Feb 14, 2020
PURPOSE: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicate...
Image Processing, Computer-Assisted Meningioma Meningeal Neoplasms Multiparametric Magnetic Resonance Imaging Humans Retrospective Studies Deep Learning
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Inconsistent Performance of Deep Learning Models on Mammogram Classification.

Journal of the American College of Radiology : JACR Feb 14, 2020
OBJECTIVES: Performance of recently developed deep learning models for image classification surpasses that of radiologists. However, there are questions about model performance consistency and generalization in unseen external data. The purpose of th...
Early Detection of Cancer Breast Neoplasms Mammography Deep Learning Image Processing, Computer-Assisted Humans Female
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