AIMC Topic:
Image Interpretation, Computer-Assisted
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A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.
Journal of medical systems
Nov 9, 2017
The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automa...
Learning a variational network for reconstruction of accelerated MRI data.
Magnetic resonance in medicine
Nov 8, 2017
PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.
Interactive phenotyping of large-scale histology imaging data with HistomicsML.
Scientific reports
Nov 6, 2017
Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundre...
Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.
Journal of neuroscience methods
Nov 2, 2017
BACKGROUND: Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Re...
A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.
Computer assisted surgery (Abingdon, England)
Oct 26, 2017
BACKGROUND AND OBJECTIVE: Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MR...
A deep convolutional neural network for classification of red blood cells in sickle cell anemia.
PLoS computational biology
Oct 19, 2017
Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological...
Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.
Journal of magnetic resonance imaging : JMRI
Oct 16, 2017
BACKGROUND: Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentiall...
Breast
Diffusion
Motion
Image Interpretation, Computer-Assisted
Receptor, ErbB-2
Estrogen Receptor alpha
Diffusion Magnetic Resonance Imaging
Echo-Planar Imaging
Machine Learning
Breast Neoplasms
Middle Aged
Reproducibility of Results
Algorithms
Image Processing, Computer-Assisted
Humans
Aged
Prospective Studies
Support Vector Machine
Adult
Female
Cervical cancer histology image identification method based on texture and lesion area features.
Computer assisted surgery (Abingdon, England)
Oct 16, 2017
The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise o...
An ensemble deep learning based approach for red lesion detection in fundus images.
Computer methods and programs in biomedicine
Oct 14, 2017
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical ...
Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images.
IEEE transactions on bio-medical engineering
Oct 13, 2017
OBJECTIVE: Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task.
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