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This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices,...
This paper shows a machine learning approach based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) to compare the diagnostic accuracy on very early Alzheimer's Disease (AD) patients with 18F FDG and Pittsburg Compound B (PiB) PET imaging. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset is used for testing, making use of the longitudinal character. Mild Cognitive...
The characterization and quantitative description of histological images is not a simple problem. To reach a final diagnosis, usually the specialist relies on the analysis of characteristics easily observed, such as cells size, shape, staining and texture, but also depends on the hidden information of tissue localization, physiological and pathological mechanisms, clinical aspects, or other etiological...
Next-generation ultrasound contrast agents, in the form of tiny gas bubbles, can be targeted to selectively adhere to cancer cells. The number of attached microbubbles could be correlated with the status of the cancer. Consequently, the estimation of bubble concentration can provide useful medical information in addition to ultrasound molecular imaging. In this paper, a method to obtain the ultrasound...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
Clinical diagnosis of voice disorders is based on examination of the oscillating vocal folds during phonation with state-of-the-art endoscopic high-speed cameras. Commonly, the offline analysis is performed in a subjective and time-consuming manner via slow-motion playback. In this study an objective method for overcoming this drawback is presented being based on phonovibrogram (PVG) images. For a...
In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. As a new kind of machine learning, support vector machine (SVM) based on statistical learning theory (SLT) has high generalization ability, especially for dataset with small number of samples in high dimensional space. SVM was originally developed...
This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric...
Support vector machine (SVM) can be seen as a new machine learning way which is based on the idea of VC dimensions and the principle of structural risk minimization rather than empirical risk minimization. SVM can be used for classification and regression. Support vector regression (SVR) is a very important branch of Support vector machine. Partial differential equations (PDEs) have been successfully...
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