A Tutorial on Deep Learning Research in

Alzheimer’s Disease – Part 1

Hoang (Mark) Nguyen

University of Missouri at Kansas City

HighlightI. Machine learning basics

1. Learning Algorithms

2. Training, Validation, Testing

3. Overfiiting vs underfitting

4. The curse of dimensionality

II. Deep learning introduction

1. Hyper-parameter

2. Convolutional neural network

3. Recurrent neural network

III. Deep learning in ADNI

1. Overview of recent publication

2. Challenges

3. Future direction

IV. Summary

Deep learning in ADNI - Overview

AD vs CN sMCI vs pMCI MCI vs CN Multi-class

The Impact of Multi-Optimizers and Data

Augmentation on TensorFlow Convolutional

Neural Network PerformanceACC = 1.0 - - -

Non-white matter tissue extraction and deep

convolutional neural network for Alzheimer’s

disease detectionACC = 0.86 - ACC = 0.86 ACC = 0.86

Deep fusion pipeline for mild cognitive

impairment diagnosisACC=0.76 - ACC=0.75 ACC=0.76

Multi-Modality Cascaded Convolutional Neural

Networks for Alzheimer’s Disease DiagnosisACC=0.85 ACC=0.74 - -

Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation , Junhao , Elina Thibeau-Sutre , Mauricio Diaz-Meloe , Jorge Samper-Gonzáleze , Alexandre Routiere, Simona Bottanie , Didier Dormonte, Stanley Durrlemane , Ninon Burgos , Olivier Colliot

Artificial Intelligence

Artificial Intelligence

Machine learning

Deep learning

Machine learningStudy of computer algorithms that build a mathematical model based on data to

make the prediction or decision for needed tasks

Classification

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

Regression

Machine learning (cont)Learning Algorithm: A process of acquiring the ability to

perform certain task based on sample data

Learning Task: is it me?

Machine learning (cont)

Original Data

Training set

Validation set

Machine leaning

Algorithm

Training

Tuning

Testing set Predictive ModelEvaluation

Machine learning (cont)

https://www.geeksforgeeks.org/regularization-in-machine-learning/

Machine learning (cont)

• The Curse of dimensionality: the problems become

exponentially difficult when the number of relevant

dimensions grows higher

https://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/

Machine learning (Before deep learning)

Data Feature Model

Healthy

Unhealthy

Reduced feature

Deep learning introduction

Deep learning introduction

• Regularization

– Parameter norm penalties

– Dataset augmentation

– Dropout

• Optimization

– Learning rate

– Parameter initialization

Deep learning introduction (cont)

Global minimum

Local minimum

LR too smallLR too big

Learning rate: step size of each iteration

Deep learning introduction (cont)

Batch size is the number of data sample for each iteration

Deep Learning Model

Batch Batch Batch Batch

GPU

Deep learning introduction (cont)

Input OutputHidden layer

Deep learning introduction (cont)

Dropout refers to ignoring certain neural network units

Deep learning introduction

https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/

Deep learning introduction (CNN)

https://www.techkingdom.org/single-post/2017/11/07/Machine-Learning-with-Python-Image-Classifier-using-VGG16-Model---Coming-Soon

Deep learning introduction (CNN)

Classification

Healthy

tuberculosis

Segmentation

Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep LearningLi Sun, Songtao Zhang, Hang Chen, and Lin Luo

Object detection

Deep learning introduction (RNN)

FeatureFeature Feature Feature

Input

Feature extraction

RNN

Output I am a teacher

Deep learning introduction (RNN + CNN)

CNN

FeatureCNN

FeatureCNN

FeatureCNN

Feature

Input

CNN

RNN

Output I am a teacher

Deep learning introduction (GAN)

Input GeneratorGenerated

samples Real

samples

GeneratorDecision

Real / Fake

Update

Update

End of Part I

Thank you for listening