AIMC Topic:
Image Interpretation, Computer-Assisted
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Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model.
Computers in biology and medicine
Dec 28, 2018
Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encode...
Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass.
Echocardiography (Mount Kisco, N.Y.)
Dec 28, 2018
BACKGROUND: Although 3D echocardiography (3DE) circumvents many limitations of 2D echocardiography by allowing direct measurements of left ventricular (LV) mass, it is seldom used in clinical practice due to time-consuming analysis. A recently develo...
Reliable Label-Efficient Learning for Biomedical Image Recognition.
IEEE transactions on bio-medical engineering
Dec 27, 2018
The use of deep neural networks for biomedical image analysis requires a sufficient number of labeled datasets. To acquire accurate labels as the gold standard, multiple observers with specific expertise are required for both annotation and proofread...
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.
IEEE transactions on pattern analysis and machine intelligence
Dec 21, 2018
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few de...
Automated diagnosis of breast ultrasonography images using deep neural networks.
Medical image analysis
Dec 20, 2018
Ultrasonography images of breast mass aid in the detection and diagnosis of breast cancer. Manually analyzing ultrasonography images is time-consuming, exhausting and subjective. Automated analyzing such images is desired. In this study, we develop a...
Radiomics-Based Intracranial Thrombus Features on CT and CTA Predict Recanalization with Intravenous Alteplase in Patients with Acute Ischemic Stroke.
AJNR. American journal of neuroradiology
Dec 20, 2018
BACKGROUND AND PURPOSE: Thrombus characteristics identified on non-contrast CT (NCCT) are potentially associated with recanalization with intravenous (IV) alteplase in patients with acute ischemic stroke (AIS). Our aim was to determine the best radio...
Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network.
Medical image analysis
Dec 19, 2018
Synthesized medical images have several important applications. For instance, they can be used as an intermedium in cross-modality image registration or used as augmented training samples to boost the generalization capability of a classifier. In thi...
An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification.
Journal of medical systems
Dec 18, 2018
In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare b...
On medical application of neural networks trained with various types of data.
Bioscience trends
Dec 17, 2018
Neural networks have garnered attention over the past few years. A neural network is a typical model of machine learning that is used to identify visual patterns. Neural networks are used to solve a wide variety of problems, including image recogniti...
Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation.
IEEE journal of biomedical and health informatics
Dec 11, 2018
Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of several clinical outcomes. In this paper, we prop...
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