AIMC Topic:
Image Interpretation, Computer-Assisted
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Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.
International journal of neural systems
Apr 4, 2016
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the constr...
Cognitive Dysfunction
ROC Curve
Imaging, Three-Dimensional
Humans
Unsupervised Machine Learning
Brain
Female
Aged
Male
Disease Progression
Datasets as Topic
Early Diagnosis
Pattern Recognition, Automated
Gray Matter
Area Under Curve
Alzheimer Disease
Positron-Emission Tomography
Image Interpretation, Computer-Assisted
Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.
Magma (New York, N.Y.)
Mar 30, 2016
OBJECTIVES: To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.
Segmenting Retinal Blood Vessels With Deep Neural Networks.
IEEE transactions on medical imaging
Mar 24, 2016
The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of patholo...
Texture- and deformability-based surface recognition by tactile image analysis.
Medical & biological engineering & computing
Mar 23, 2016
Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-com...
Adaptable texture-based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma.
Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Mar 16, 2016
BACKGROUND: Pink blush is a common feature in basal cell carcinoma (BCC). A related feature, semitranslucency, appears as smooth pink or orange regions resembling skin color. We introduce an automatic method for detection of these features based on s...
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
IEEE transactions on medical imaging
Mar 7, 2016
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...
Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles.
IEEE journal of biomedical and health informatics
Mar 4, 2016
Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of...
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.
IEEE transactions on medical imaging
Mar 4, 2016
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging...
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network.
IEEE transactions on medical imaging
Feb 29, 2016
Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep l...
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.
IEEE transactions on bio-medical engineering
Feb 26, 2016
GOAL: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.
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