C4.5: Programs for Machine Learning (書評)

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. 
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

An overview of deep learning in medical imaging focusing on MRI

From the Publisher: 
This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search performed by most rule induction methods.

Special Issue on data mining and knowledge discovery with evolutionary algorithms

Thank you very much for downloading using mpi portable parallel programming with the message passing interface. As you may know, people have search hundreds times for their chosen novels like this using mpi portable parallel programming with the message passing interface, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their laptop.

Using Mpi Portable Parallel Programming With The Message Passing Interface

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.

/pdf/physiologically-based-modeling-of-3-d-vascular-networks-and-sjnfum2o14.pdf

Physiologically based modeling of 3-D vascular networks and CT scan angiography

We describe EDRL-MD, an evolutionary algorithm-based system, for learning decision rules from databases. The main novelty of our approach lies in dealing with continuous – valued attributes. Most of decision rule learners use univariate discretization methods, which search for threshold values for one attribute at the same time. In contrast to them, EDRL-MD simultaneously searches for threshold values for all continuous-valued attributes, when inducing decision rules. We call this approach multivariate discretization. Since multivariate discretization is able to capture interdependencies between attributes it may improve the accuracy of obtained rules. The evolutionary algorithm uses problem specific operators and variable-length chromosomes, which allows it to search for complete rulesets rather than single rules. The preliminary results of the experiments on some real-life datasets are presented.

/pdf/an-evolutionary-algorithm-using-multivariate-discretization-19wh8ty1y2.pdf

An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction

In the paper, a new evolutionary approach to induction of oblique decision trees is described. In each non-terminal node, the specialized evolutionary algorithm is applied to search for a splitting hyper-plane. The feature selection is embedded into the algorithm, which allows to eliminate redundant and noisy features at each node. The experimental evaluation of the proposed approach is presented on both synthetic and real datasets.

/pdf/an-evolutionary-algorithm-for-oblique-decision-tree-dsy0lkdb7w.pdf

An Evolutionary Algorithm for Oblique Decision Tree Induction

Graphical abstractDisplay Omitted HighlightsWe investigate the role of regression tree representation.A new EA for decision tree induction with heterogeneous representation is studied.Inducer that is capable to self-adapt to the analyzed data is proposed. A regression tree is a type of decision tree that can be applied to solve regression problems. One of its characteristics is that it may have at least four different node representations; internal nodes can be associated with univariate or oblique tests, whereas the leaves can be linked with simple constant predictions or multivariate regression models. The objective of this paper is to demonstrate the impact of particular representations on the induced decision trees. As it is difficult if not impossible to choose the best representation for a particular problem in advance, the issue is investigated using a new evolutionary algorithm for the decision tree induction with a structure that can self-adapt to the currently analyzed data. The proposed solution allows different leaves and internal nodes representation within a single tree. Experiments performed using artificial and real-life datasets show the importance of tree representation in terms of error minimization and tree size. In addition, the presented solution managed to outperform popular tree inducers with defined homogeneous representations.

The role of decision tree representation in regression problems - An evolutionary perspective

This paper shows the influence of computed tomography slice thickness on textural parameters by simulating realistic images issued from: (1) a 3D model of vascular tree, with structural and functional features and in which angiogenesis is related to the organ growth; (2) a projection/reconstruction process using fast Fourier transform. Texture analysis is performed by means of second-order statistics and gradient based methods.

Marek Kretowski

Papers

Physiologically based modeling of 3-D vascular networks and CT scan angiography

An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction

An Evolutionary Algorithm for Oblique Decision Tree Induction

The role of decision tree representation in regression problems - An evolutionary perspective

Toward a better understanding of texture in vascular CT scan simulated images