AIMC Topic:
Image Interpretation, Computer-Assisted
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Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.
The British journal of radiology
May 14, 2019
OBJECTIVE: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-...
The Utility of Applying Various Image Preprocessing Strategies to Reduce the Ambiguity in Deep Learning-based Clinical Image Diagnosis.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
May 10, 2019
PURPOSE: A general problem of machine-learning algorithms based on the convolutional neural network (CNN) technique is that the reason for the output judgement is unclear. The purpose of this study was to introduce a strategy that may facilitate bett...
Automated segmentation of macular edema in OCT using deep neural networks.
Medical image analysis
May 10, 2019
Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema becau...
Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.
IEEE transactions on bio-medical engineering
May 9, 2019
OBJECTIVE: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imb...
Putting machine learning into motion: applications in cardiovascular imaging.
Clinical radiology
May 9, 2019
Heart and circulatory diseases cause a quarter of all deaths in the UK and cardiac imaging offers an effective tool for early diagnosis and risk-stratification to improve premature death and disability. This domain of radiology is unique in that asse...
Precision diagnostics based on machine learning-derived imaging signatures.
Magnetic resonance imaging
May 6, 2019
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic a...
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders.
IEEE transactions on medical imaging
May 6, 2019
Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relat...
Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning.
Atherosclerosis
May 4, 2019
BACKGROUND AND AIMS: Although grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent implantation, detection of vulnerable plaques by IVUS remains challenging. We aimed to develop mach...
Neural Networks, Computer
Machine Learning
Female
Fibrosis
Male
Humans
Image Interpretation, Computer-Assisted
Middle Aged
Coronary Vessels
Bayes Theorem
Risk Assessment
Ultrasonography, Interventional
Coronary Stenosis
Disease Progression
Rupture, Spontaneous
Coronary Artery Disease
Reproducibility of Results
Diagnosis, Computer-Assisted
Plaque, Atherosclerotic
Risk Factors
Predictive Value of Tests
Aged
Tomography, Optical Coherence
Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.
Journal of magnetic resonance imaging : JMRI
May 2, 2019
BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging.
Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.
Radiology
Apr 30, 2019
Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for...
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