reviewing convolutional neural networks for image processing especially for medical diagnosis and introducing the new CNN architect is following.

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Diagnosis of alzheimer's disease
with deep learning
2016. 7. 4
Seonho Park

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Outline
Introduction to Machine Learning
Convolutional Neural Network
Diagnosing of Alzheimer’s disease

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Introduction to Machine Learning
Convolutional Neural Network
Diagnosing of Alzheimer’s disease

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Introduction to Machine Learning
x1
x2
x1
y
x1
x2
<Supervised Learning> <Unsupervised Learning>
classification regression clustering
Category of Machine Learning
문제 + 정답
문제 + 정답
문제 + 정답
데이터 + 레이블 머신러닝 학습
머신러닝 모델 정답 예측새로운 데이터
문제 + 정답
문제 + ???
분류 회귀
Cat
Computer
Lion
Pencil
Pig
레이블 없는 데이터 머신러닝 학습 군집화

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Introduction to Machine Learning
Scikit-Learn
• Machine Learning Library in Python
• http://scikit-learn.org/
• Classification: Decision trees, SVM, NN
• Regression: GP, Ordinary LS, Ridge Regression, SVR
• Clustering: k-Means, Spectral Clustering

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Introduction to Machine Learning
Why Deep Learning?
• Deep Learning = Deep Neural Network
• Data and Machine Learning
† http://cs229.stanford.edu/materials/CS229-DeepLearning.pdf

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Introduction to Machine Learning
Artificial neural networks
Training = Find weights (parameters)
Inference = get output by specific input and trained weights

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Introduction to Machine Learning
Convolutional Neural Network (CNN)
• Image Processing (Computer Vision)

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Introduction to Machine Learning
Recurrent Neural Network (RNN)
• Time Series Data
• Natural Language Processing
• Translation, Speech Recognition, Auto Caption
• 자동번역, 음성인식, 이미지 캡션 생성 등에 활용
† Towards End-to-End Speech Recognition with Recurrent Neural Networks, Alex Graves et al (2014)

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Introduction to Machine Learning
Why GPU?
• CuDNN: GPU-accelerated library of primitives for deep neural networks
• VRAM limitation, Double/Single/Half Precision
• Linear Algebra: CuBLAS, MAGMA

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Introduction to Machine Learning
Frameworks
Cuda-ConvNetPylearn2
Lasagne

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Introduction to Machine Learning
Open Sources for Deep Learning
† Comparative Study of Deep Learning Software Frameworks, Soheil Bahrampour et al (2015)

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Introduction to Machine Learning
Pioneers
• Yann Lecun
• Geoffrey Hinton
• Yoshua Bengio
• Andrew Ng
• Jürgen Schmidhuber

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Image Recognition Speech Recognition Auto Caption
Self Driving Car Natural Language Processing Recommendation System
Introduction to Machine Learning
Applications

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Introduction to Machine Learning
Convolutional Neural Network
Diagnosing of Alzheimer’s disease

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Convolutional Neural Network
Overview
• Classification
• Convolution Operation + MLP
• Architecture
• Convolutional Layer (Convolution Operator, Activation)
• Subsampling (Downsampling, Pooling)
• Fully Connected Layer
• Classifier

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Convolutional Neural Network
LeNet5† Convolutional Operation
† Gradient Based Learning Applied to Document Recognition
, Yann LeCun et al (1998)
• Digit Recognition • Weight matrix (filter): 4D tensor
[# of feature at layer m,
# of features at layer m-1,
height, width]

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Convolutional Neural Network
Activation function (nonlinearity)
† Systematic evaluation of CNN advances on the ImageNet, Dmytro Mishkin, et al (2016)

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Convolutional Neural Network
Pooling Layer
• Erase Noise
• Reduce Feature Map Size (Memory Save)
† Systematic evaluation of CNN advances on the ImageNet, Dmytro Mishkin, et al (2016)

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Convolutional Neural Network
Training
• Error(Loss) Function: Categorical Cross Entropy
• Design Variable: weights(W), bias(b)
• Backpropagation
conjunction with an optimization method
such as gradient descent
• Vanishing gradient

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Convolutional Neural Network
Mini-Batch Method
• Computational Efficiency
• Memory Use
• Iteration & Epoch
Vanilla Gradient Descent
Stochastic Gradient Descent
• Parameter update for each training example x(i) and label y(i)
• Step size(η) is typically set to 10-3

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Convolutional Neural Network
Training (Optimization)
• Update Functions
• Second-order Method (L-BFGS) is not common in practice
• NAG is more standard

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Convolutional Neural Network
Overfitting and Regularization
• Dropout
• Relaxation: Add Regularization Term to Loss Function
• Remove Layer (Reduce Parameters), Add Feature
† Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava et al (2014)

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Convolutional Neural Network
Local Optimum?
† Identifying and attacking the saddle point problem in high-dimensional non-convex optimization, Yann N. Dauphin et al (2014)
• Non-convex optimization problem
• deeper and more profound difficulty originates from the proliferation of saddle points, not
local minima, especially in high dimensional problems of practical interest

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Convolutional Neural Network
Parallel Computation
• Architectural Parallel: Divide Channel
• Data Parallel: Divide Batch

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ILSVRC
• Evaluate algorithms for object detection and image classification at large scale
• Training: 1.3M/ Test: 100k, 1000 categories
Convolutional Neural Network

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AlexNet
• ILSVRC12 1st Place
• 15.3% error rate (2nd place achieved 26.5% error rate)
• Architecture Parallel (2GPU used)
† ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky et al. (2012)
Convolutional Neural Network

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VGG Net
• DeepMind
• ILSVRC14 2nd Place
• 6.8% error rate
† VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION, Karen Simonyan et al. (2014)
Convolutional Neural Network

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GoogLeNet
• Google
• Inception module
• ILSVRC14 1st Place
• 6.67% error rate
† Going Deeper with Convolutions, Christian Szegedy et al. (2014)
Convolutional Neural Network

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MSRA
• MicroSoft
• PReLU activation
• Weight initialization
• 4.94% error rate (Surpass Human Level, 5.1%)
† Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He et al. (2015)
Convolutional Neural Network

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Inception-v3
• Google
• Inception Module Upgrade
• 50 GPUs
• 3.46% error rate
• Public Use with TensorFlow
† Going Deeper with Convolutions, Christian Szegedy et al. (2015)
Convolutional Neural Network

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Convolutional Neural Network
Deep Neural Networks are Easily Fooled†
† Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, A Nguyen et al (2014)
• It is possible to produce images totally unrecognizable to
human eyes
• interesting differences between human vision and current DNNs
• raise questions about the generality of DNN computer vision

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Convolutional Neural Network
Neural Style
† A Neural Algorithm of Artistic Style, Leon A. Gatys et al (2014)
• Style + Contents reconstruction
• Caffe framework
• https://github.com/jcjohnson/neural-style

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Introduction to Machine Learning
Convolutional Neural Network
Diagnosing of Alzheimer’s disease

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Diagnosing of Alzheimer’s disease
Traditional Diagnosis of Alzheimer’s disease
• Review medical history
• Mini Mental Status Exam
• Physical Exam
• Neurological Exam
• Brain Image: Structural(MRI,CT), Functional(fMRI)
• NC(Normal Condition), MCI(Mild Cognitive Impairment), AD
• AD: Vascular/Non-Vascular

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Diagnosing of Alzheimer’s disease
AD Patients’ MRI Features
• Temporal Lobe: Hippocampus
• Ventricle

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Diagnosing of Alzheimer’s disease
Case Study: Machine Learning for diagnosing of AD
• PET, MRI images
• Patch Extraction
• Restrict Bolzmann Machine
• Accuracy: 92.4%(MRI), 95.35%(MRI+PET)
† Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis, Heung-Il Suk et al (2014)

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Diagnosing of Alzheimer’s disease
Case Study: Machine Learning for diagnosing of AD
• Feature: Cortex Thickness
• FreeSurfer
• Linear discriminant analysis (LDA)
• Accuracy: Sensitivity: 82%, Specificity: 93%
† Individual subject classification for Alzheimer’s disease based on incremental learning using a
spatial frequency representation of cortical thickness data, Young-Sang Cho et al (2012)

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Diagnosing of Alzheimer’s disease
Preprocessing
• Data Set: about 1400 of T1 MRI from SMC
• FreeSurfer: Skull Stripping: reduce size [256,256,256][190,190,190] / 67MB27MB
• Pixel Value Normalization [0,255]  [-1,1]
• Mirrored cropping

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Diagnosing of Alzheimer’s disease
Architecture
• CNN
• Lasagne (Theano) Framework
• Inception Module, Batch Normalization
• 3D Convolution + CuDNN v3 (Github)
• 2 TITAN X GPU: Data Parallel (PyCUDA)
• Batch Size: 80
• Training Set
#Healthy Condition(HC): 761
#Alzheimer’s Disease (AD): 389
• Test Set
#Healthy Condition(HC): 105
#Alzheimer’s Disease (AD): 84
Data

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Diagnosing of Alzheimer’s disease
Architecture
input
24*Conv11/5
MaxPool7/2
288*Conv3/2
FC120
DropOut
SoftMax
input
36*Conv16/6
MaxPool3/2
120*Conv4/1
BatchNorm
MaxPool3/2
60*Conv1/1
96*Conv3/1
12*Conv1/1
24*Conv5/124*Conv1/1
MaxPool3/1
48*Conv1/1
Concatenate
MaxPool3/2
FC150
128*Conv1/1
192*Conv3/1
32*Conv1/1
96*Conv5/164*Conv1/1
MaxPool3/1
128*Conv1/1
Concatenate
96*Conv1/1
208*Conv3/1
16*Conv1/1
48*Conv5/164*Conv1/1
MaxPool3/1
192*Conv1/1
Concatenate
SoftMax
input
60*Conv10/2
MaxPool2/2
144*Conv3/1
BatchNorm
MaxPool3/2
48*Conv1/1
72*Conv3/1
18*Conv1/1
36*Conv5/148*Conv1/1
MaxPool3/1
48*Conv1/1
Concatenate
MaxPool3/2
FC500
96*Conv1/1
208*Conv3/1
16*Conv1/1
48*Conv5/164*Conv1/1
MaxPool3/1
192*Conv1/1
Concatenate
160*Conv1/1
320*Conv3/1
32*Conv1/1
128*Conv5/1128*Conv1/1
MaxPool3/1
256*Conv1/1
Concatenate
SoftMax
280*Conv1/1
340*Conv3/1
32*Conv1/1
128*Conv5/1128*Conv1/1
MaxPool3/1
228*Conv1/1
Concatenate
AvgPool3/1
MidasNet1
MidasNet2
MidasNet3

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Convergence History
Model Accuracy
MidasNet1 167/189 (88.4%)
MidasNet2 169/189 (89.4%)
MidasNet3 169/189 (89.4%)
0.01
0.1
1
10
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
Cost
Epoch
Diagnosing of Alzheimer’s disease
Result

43. Thank You