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

Advances in multimodality data fusion in neuroimaging

Simple Summary Deep learning has become a popular technique in modern computer-aided (CAD) systems. In breast cancer CAD systems, breast pectoral segmentation is an important procedure to remove unwanted pectoral muscle in the images. In recent decades, there are numerous studies aiming at developing efficient and accurate methods for pectoral muscle segmentation. However, some methods heavily rely on manually crafted features that can easily lead to segmentation failure. Moreover, deep learning-based methods are still suffering from poor performance at high computational costs. Therefore, we propose a novel deep learning segmentation framework to provide fast and accurate pectoral muscle segmentation result. In the proposed framework, the novel network architecture enables more useful information to be used and therefore improve the segmentation results. The experimental results using two public datasets validated the effectiveness of the proposed network. Abstract As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46%, global pixel accuracy of 99.48%, Dice similarity coefficient of 96.30%, and Jaccard of 93.33%, respectively.

/pdf/pemnet-for-pectoral-muscle-segmentation-1udhkz8p.pdf

PeMNet for Pectoral Muscle Segmentation

Alzheimer's Diasese (AD) diagnosis can be carried out by analysing functional or structural changes in the brain. Functional changes associated to neurological disorders can be figured out by positron emission tomography (PET) as it allows to study the activation of certain areas of the brain during specific task development. On the other hand, neurological disorders can also be discovered by analysing structural changes in the brain which are usually assessed by Magnetic Resonance Imaging (MRI). In fact, computer-aided diagnosis tools (CAD) that have been recently devised for the diagnosis of neurological disorders use functional or structural data. However, functional and structural data can be fused out in order to improve the accuracy and to diminish the false positive rate in CAD tools. In this paper we present a method for the diagnosis of AD which fuses multimodal image (PET and MRI) data by combining Sparse Representation Classifiers (SRC). The method presented in this work shows accuracy values up to 95% and clearly outperforms the classification outcomes obtained using single-modality images.

/pdf/multimodal-image-data-fusion-for-alzheimer-s-disease-beq1y9abmm.pdf

Multimodal image data fusion for Alzheimer's Disease diagnosis by sparse representation.

Alzheimer's disease (AD) is the most common cause of dementia. Nowadays, 44 million people worldwide suffer from this neurodegenerative disease. Fortunately, the use of new technologies can help doctors in diagnosing this disease in an increasingly early stage, which is vital to prevent its advance. In this work we have developed a new automatic method to predict if patients suffering from mild cognitive impairment (MCI) will develop AD within one year or, conversely, its impairment will remain stable. This technique is based on the so-called Searchlight, a widely known approach in fMRI but which has not been previously used with structural images. Besides analyzing the intensity of the voxels in each of the subregions defined by the Searchlight, data from two neuro-psychological tests were used during the classification process, achieving an accuracy of 84%.

Improving short-term prediction from MCI to AD by applying searchlight analysis

We present a novel classification method of SPECT images based on clustering for the diagnosis of Alzheimer's disease. The aims of the clustering approach which is based on Gaussian Mixture Model (GMM) for density estimation, is to automatically select Regions of Interest (ROIs) and to effectively reduce the dimensionality of the problem. The clusters represented by Gaussians are constructed according to a maximum likelihood criterion employing the expectation maximization (EM) algorithm. By considering only the intensity levels inside the clusters, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features. With this feature extraction method one avoids the so-called small sample size problem and nonlinear classifiers may be used to distinguish between the brain images of normal and Alzheimer patients. Our results show that for various classifiers the clustering method yields higher accuracy rates than the classification considering all voxel values.

Juan Manuel Górriz

Papers

Advances in multimodality data fusion in neuroimaging

PeMNet for Pectoral Muscle Segmentation

Multimodal image data fusion for Alzheimer's Disease diagnosis by sparse representation.

Improving short-term prediction from MCI to AD by applying searchlight analysis

Classification of SPECT Images Using Clustering Techniques Revisited