다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Nowadays, 35 million people worldwide suffer from some form of dementia. Given the increase in life expectancy it is estimated that in 2035 this number will grow to 115 million. Alzheimer’s disease is the most common cause of dementia and it is of great importance diagnose it at an early stage. This is the main goal of this work, the development of a new automatic method to predict the mild cognitive impairment (MCI) patients who will develop Alzheimer’s disease within one year or, conversely, its impairment will remain stable. This technique will analyze data from both magnetic resonance imaging and neuropsychological tests by utilizing a t-test for feature selection, maximum-uncertainty linear discriminant analysis (MLDA) for classification and leave-one-out cross validation (LOOCV) for evaluating the performance of the methods, which achieved a classification accuracy of 73.95 %, with a sensitivity of 72.14 % and a specificity of 73.77 %.

Short-term Prediction of MCI to AD Conversion Based on Longitudinal MRI Analysis and Neuropsychological Tests

In this work, a novel approach to Computer Aided Diagnosis (CAD) system for the Parkinson’s Disease (PD) is proposed. This tool is intended for physicians, and is based on fully automated methods that lead to the classification of Ioflupane/FP-CIT-I-123 (DaTSCAN) SPECT images. DaTSCAN images from the Parkinson Progression Markers Initiative (PPMI) are used to have in vivo information of the dopamine transporter density. These images are normalized, reduced (using a mask), and then a GLC matrix is computed over the whole image, extracting several Haralick texture features which will be used as a feature vector in the classification task. Using the leave-one-out cross-validation technique over the whole PPMI database, the system achieves results up to a 95.9% of accuracy, and 97.3% of sensitivity, with positive likelihood ratios over 19, demonstrating our system’s ability on the detection of the Parkinson’s Disease by providing robust and accurate results for clinical practical use, as well as being fast and automatic.

Texture Features Based Detection of Parkinson's Disease on DaTSCAN Images

Alzheimer's Disease (AD) is a progressive neurologic disease of the brain that leads to the irreversible loss of neurons and dementia The new brain imaging techniques PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) provide functional information about the brain activity and have been widely used in the AD diagnosis process However, the diagnosis currently relies on manual image reorientations, visual evaluation and other subjective, time consuming steps In this work, a complete computer aided diagnosis (CAD) system is developed to assist the clinicians in the AD diagnosis process It is based on bayesian classifiers, made up from features previously extracted The small size sample problem, consisting of having a number of available samples much lower than the dimension of the feature space, is faced up by applying Principal Component Analysis (PCA) to the features This approach provides higher accuracy values than other previous approaches do, yielding 9121% and 9833% accuracy values for SPECT and PET images, respectively

/pdf/automatic-system-for-alzheimer-s-disease-diagnosis-using-qnbhqlztdk.pdf

Automatic System for Alzheimer's Disease Diagnosis Using Eigenbrains and Bayesian Classification Rules

This work shows an Association Rule (AR)-based approach in order to design a computer aided diagnosis (CAD) system for the Alzheimer's disease (AD) detection with a 18F-FDG and Pittsburg Compound B (PiB) PET (Positron Emission Tomography) biomarker analysis. The AD Neuroimaging Initiative (ADNI) 1 dataset is used for testing and a comparison is conducted in two different sets: controls versus AD or controls versus Mild Cognitive Impairment (MCI) subjects. 3D Regions of Interest (ROls) are obtained with an activation estimation method for both biomarkers considered independently or in a combined way without repetition. These ROls are used as input for an Apriori algorithm in order to obtain ARs from controls. Classification is performed taking into account the percentage of verified rules by each subject. Although PiB is distinguished from FDG in providing differential information, the combination of biomarkers shows positive results for both sets: while in the first, FDG results get better reaching an accuracy of 94.74%, in the second set both FDG and PIB rates are improved reaching a maximum accuracy of 92.86%; outperforming the results of other recently reported methods.

http://ieeexplore.ieee.org/iel7/6531926/6551044/06551589.pdf

Juan Manuel Górriz

Papers

Short-term Prediction of MCI to AD Conversion Based on Longitudinal MRI Analysis and Neuropsychological Tests

Texture Features Based Detection of Parkinson's Disease on DaTSCAN Images

Automatic System for Alzheimer's Disease Diagnosis Using Eigenbrains and Bayesian Classification Rules

FDG and PIB biomarker PET analysis for the Alzheimer's disease detection using Association Rules

Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging