MACHINE LEARNING : methods and applications to brain disorders.

Format:

Book

Language:

English

Physical Description:

1 online resource

Place of Publication:

[Place of publication not identified] : ELSEVIER ACADEMIC PRESS, 2019.

System Details:

text file

Contents:

Part 1

1 Introduction to machine learning / Sandra Vieira and Walter Hugo Lopez Pinaya and Andrea Mechelli

1.2 From human learning to machine learning p. 2

1.3 What is machine learning? p. 4

1.4 How is machine learning relevant to brain disorders? p. 5

1.5 Different types of machine learning p. 9

2 Main concepts in machine learning / Sandra Vieira and Walter Hugo Lopez Pinaya and Andrea Mechelli

2.2 Problem formulation p. 22

2.3 Data preparation p. 23

2.4 Feature engineering p. 23

2.5 Model training p. 28

2.6 Model evaluation p. 36

2.7 Post hoc analysis p. 41

3 Applications of machine learning to brain disorders / Cristina Scarpazza and Lea Baecker and Sandra Vieira and Andrea Mechelli

3.2 Why are people interested in machine learning? p. 45

3.3 What are the main challenges in machine learning studies of psychiatric and neurological disorders? p. 50

3.4 How good is good enough? p. 55

3.5 Is machine learning ready to be applied in psychiatry and neurology? p. 57

3.6 Future directions and concluding remarks p. 59

Part 2

4 Linear regression / Thomas M.H. Hope

4.2 Method description p. 69

4.3 Applications to brain disorders p. 74

5 Linear methods for classification / Andre F. Marquand and Seyed Mostafa Kia

5.2 Method description p. 85

5.3 Applications to brain disorders p. 91

6 Support vector machine / Derek A. Pisner and David M. Schnyer

6.2 Method description p. 102

6.3 Applications to brain disorders p. 109

7 Support vector regression / Fan Zhang and Lauren J. O'Donnell

7.2 Method description p. 125

7.3 Applications to brain disorders p. 131

8 Multiple kernel learning / Letizia Squarcina and Umberto Castellani and Paolo Brambilla

8.2 Method description p. 143

8.3 Applications to brain disorders p. 148

9 Deep neural networks / Sandra Vieira and Walter Hugo Lopez Pinaya and Rafael Garcia-Dias and Andrea Mechelli

9.2 Method description p. 159

9.3 Applications to brain disorders p. 166

10 Convolutional neural networks / Walter Hugo Lopez Pinaya and Sandra Vieira and Rafael Garcia-Dias and Andrea Mechelli

10.2 Method description p. 175

10.3 Applications to brain disorders p. 184

11 Autoencoders / Walter Hugo Lopez Pinaya and Sandra Vieira and Rafael Garcia-Dias and Andrea Mechelli

11.2 Method description p. 194

11.3 Applications to brain disorders p. 201

12 Principal component analysis / Ferath Kherif and Adeliya Latypova

12.2 Method description p. 212

12.3 Applications to brain disorders p. 218

13 Clustering analysis / Rafael Garcia-Dias and Sandra Vieira and Walter Hugo Lopez Pinaya and Andrea Mechelli

13.2 Method description p. 230

13.3 Applications to brain disorders p. 240

Part 3

14 Dealing with missing data, small sample sizes, and heterogeneity in machine learning studies of brain disorders / Rajat M. Thomas and Willem Bruin and Paul Zhutovsky and Guido van Wingen

14.2 Data simulation p. 251

14.3 Algorithms and procedures p. 253

15 Working with high-dimensional feature spaces: the example of voxel-wise encoding models / Mohammad Babakmehr and Ghislain St-Yves and Thomas Naselaris

15.2 Voxel-wise encoding- modeling p. 268

15.3 Applications to brain disorders p. 278

16 Multimodal integration / Sandra Vieira and Walter Hugo Lopez Pinaya and Rafael Garcia-Dias and Andrea Mechelli

16.2 Early multimodal data integration: data fusion p. 287

16.3 Intermediate multimodal integration: kernel-based methods and deep learning p. 291

16.4 Late multimodal integration: ensemble methods p. 294

16.5 Application to brain disorders p. 296

17 Bias, noise, and interpretability in machine learning: from measurements to features / Hugo Schnack

17.2 Main sources of bias and noise in machine learning p. 309

17.3 Data processing p. 311

17.4 Applications to brain disorders p. 318

18 Ethical issues in the application of machine learning to brain disorders / Philipp Kellmeyer

18.2 Applications of machine learning to brain disorders p. 330

18.3 Ethical tensions from using machine learning in brain disorders p. 331

Part 4

19 A step-by-step tutorial on how to build a machine learning model / Sandra Vieira and Rafael Garcia-Dias and Walter Hugo Lopez Pinaya

19.2 Installing Python and main libraries p. 344

19.3 How to read this chapter p. 345

19.4 Using brain morphometry to classify patients with schizophrenia and healthy controls p. 345.

Other Format:

ebook version :

ISBN:

9780128157404

0128157402

OCLC:

1128038540

Access Restriction:

Restricted for use by site license.