Convolutional Neural Network for Alzheimer’s disease diagnosis with Neuroimaging

1. 1 / 42 Diagnosis of alzheimer's disease with deep learning 2016. 7. 4 Seonho Park

2. 2 / 42 Outline Introduction to Machine Learning Convolutional Neural Network Diagnosing of Alzheimer’s disease

3. 3 / 42 Introduction to Machine Learning Convolutional Neural Network Diagnosing of Alzheimer’s disease

4. 4 / 42 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

5. 5 / 42 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

6. 6 / 42 Introduction to Machine Learning Why Deep Learning? • Deep Learning = Deep Neural Network • Data and Machine Learning † http://cs229.stanford.edu/materials/CS229-DeepLearning.pdf

7. 7 / 42 Introduction to Machine Learning Artificial neural networks Training = Find weights (parameters) Inference = get output by specific input and trained weights

8. 8 / 42 Introduction to Machine Learning Convolutional Neural Network (CNN) • Image Processing (Computer Vision)

9. 9 / 42 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)

10. 10 / 42 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

11. 11 / 42 Introduction to Machine Learning Frameworks Cuda-ConvNetPylearn2 Lasagne

12. 12 / 42 Introduction to Machine Learning Open Sources for Deep Learning † Comparative Study of Deep Learning Software Frameworks, Soheil Bahrampour et al (2015)

13. 13 / 42 Introduction to Machine Learning Pioneers • Yann Lecun • Geoffrey Hinton • Yoshua Bengio • Andrew Ng • Jürgen Schmidhuber

14. 14 / 42 Image Recognition Speech Recognition Auto Caption Self Driving Car Natural Language Processing Recommendation System Introduction to Machine Learning Applications

16. 16 / 42 Convolutional Neural Network Overview • Classification • Convolution Operation + MLP • Architecture • Convolutional Layer (Convolution Operator, Activation) • Subsampling (Downsampling, Pooling) • Fully Connected Layer • Classifier

17. 17 / 42 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]

18. 18 / 42 Convolutional Neural Network Activation function (nonlinearity) † Systematic evaluation of CNN advances on the ImageNet, Dmytro Mishkin, et al (2016)

19. 19 / 42 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)

20. 20 / 42 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

21. 21 / 42 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

22. 22 / 42 Convolutional Neural Network Training (Optimization) • Update Functions • Second-order Method (L-BFGS) is not common in practice • NAG is more standard

23. 23 / 42 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)

24. 24 / 42 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

25. 25 / 42 Convolutional Neural Network Parallel Computation • Architectural Parallel: Divide Channel • Data Parallel: Divide Batch

26. 26 / 42 ILSVRC • Evaluate algorithms for object detection and image classification at large scale • Training: 1.3M/ Test: 100k, 1000 categories Convolutional Neural Network

27. 27 / 42 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

28. 28 / 42 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

29. 29 / 42 GoogLeNet • Google • Inception module • ILSVRC14 1st Place • 6.67% error rate † Going Deeper with Convolutions, Christian Szegedy et al. (2014) Convolutional Neural Network

30. 30 / 42 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

31. 31 / 42 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

32. 32 / 42 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

33. 33 / 42 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

35. 35 / 42 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

36. 36 / 42 Diagnosing of Alzheimer’s disease AD Patients’ MRI Features • Temporal Lobe: Hippocampus • Ventricle

37. 37 / 42 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)

38. 38 / 42 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)

39. 39 / 42 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

40. 40 / 42 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

41. 41 / 42 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

42. 42 / 42 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