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

Automatic Diagnosis of Schizophrenia in EEG Signals Using Functional Connectivity Features and CNN-LSTM Model

In this paper we present an study which shows the possibility of using wavelets and wavelet packets to detect transients produced by termites. Identification has been developed by means of analyzing the impulse response of three sensors undergoing natural excitations. De-noising exhibits good performance up to SNR=-30 dB, in the presence of white Gaussian noise. The test can be extended to similar vibratory or acoustic signals resulting from impulse responses.

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Wavelets and wavelet packets applied to termite detection

This work presents a computer-aided diagnosis technique for improving the accuracy of the diagnosis of the Alzheimer’s disease (AD). Some regions of the SPECT image discriminate more between healthy and AD patients than others, thus, it is important to design an automatic tool for selecting these regions. This work shows the performance of the Mann-Whitney-Wilcoxon U-test, a non-parametric technique which allows to select voxels of interest. Those voxels with higher U values are selected and their intensity values are used as input for a Support Vector Machine classifier with linear kernel. The proposed methodology yields an accuracy greater than 90% in the diagnosis of the AD and outperforms existing techniques including the voxel-as-features approach.

Selecting Regions of Interest in SPECT Images Using Wilcoxon Test for the Diagnosis of Alzheimer's Disease

Alzheimer’s Disease (AD) is the most common neurodegenerative disease in elderly people, and current drugs, unfortunately, do not represent yet a cure. This is explained, at least in part, because the understanding of the neurodegenerative process is still incomplete, being sometimes mistaken, particularly at the first steps of the illness, with the natural ageing process. A better identification of how the functional activity deteriorates is thus crucial to develop new and more effective treatments. Sparse inverse covariance estimates (SICE) have been recently employed for deriving functional connectivity patterns from Positron Emission Tomography (PET) of brains affected by Alzheimer’s Disease. SICE, unlike the traditional covariance methods, allows to analyze the interdependencies between brain regions factoring out the influence of others. To analyze the effects of the illness, connectivity patterns of brains affected by AD are compared with those obtained for control groups. These comparisons are, however, carried out for binary adjacency matrices with the same number of arcs. Additionally, the effect of the number of subjects employed or the validity of the regularization parameter used to compute the SICE have been not analyzed. In this paper, we delves into the construction of connectivity patterns from PET using SICE. In particular, we describe the effect that the number of subjects employed has on the results and identify, based on the reconstruction error of linear regression systems, a range of valid values for the regularization parameter. The amount of arcs is also proved as a discriminant value and that should thus be taken into account for the comparisons across groups. We also show that it is possible to pass from unweighted (binary) to weighted adjacency matrices, where the weight of a connection corresponding to the existence of a relationship between two brain areas can be correlated to the persistence of this relationship when computed for different values of the regularization parameter and sets of subjects. Finally, different networks measures are computed for the connectivity patterns confirming, among other things, that SICE may be particularly apt for assessing the efficiency of drugs, since it produces reliable brain connectivity networks with small sample sizes.

/pdf/construction-and-analysis-of-weighted-brain-networks-from-3zy2gh7no0.pdf

Construction and Analysis of Weighted Brain Networks from SICE for the Study of Alzheimer's Disease

The early detection of subjects with probable Alzheimer Type Dementia (ATD) is crucial for effective appliance of treatment strategies. Functional brain imaging including SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography) are commonly used to guide the clinician's diagnosis. Nowadays, no automatic tool has been developed to aid the experts to diagnose the ATD. Instead, conventional evaluation of these scans often relies on subjective, time consuming and prone to error steps. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the ATD. The proposed approach is based on the majority voting cast by an ensemble of Support Vector Machine (SVM) classifiers, trained on a component-based feature extraction technique which searches the most discriminant regions over the brain volume.

Juan Manuel Górriz

Papers

Automatic Diagnosis of Schizophrenia in EEG Signals Using Functional Connectivity Features and CNN-LSTM Model

Wavelets and wavelet packets applied to termite detection

Selecting Regions of Interest in SPECT Images Using Wilcoxon Test for the Diagnosis of Alzheimer's Disease

Construction and Analysis of Weighted Brain Networks from SICE for the Study of Alzheimer's Disease

Automatic Classification System for the Diagnosis of Alzheimer Disease Using Component-Based SVM Aggregations