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2017 07 03_meetup_d
- 1. A World -Leading Science Foundation Ireland Research Centre Deep Learning for Medical Image Analysis Keelin Murphy July 3rd 2017
- 2. About Me BA Mathematics TCD Software Industry 4-5 years MSc + PhD in Biomedical Image Analysis UCC (INFANT Research Centre) Utrecht Medical Center, the Netherlands
- 3. Today…. Machine Learning Deep Learning Deep Learning for Image Analysis Deep Learning for Medical Image Analysis Deep Learning for Neonatal Medical Image Analysis
- 4. Artificial Deep Intelligent Machine Learning …..? Deep Learning Machine Learning Artificial Intelligence
- 5. Machine Learning Finding patterns in data Supervised learning – Learn by example, like humans
- 6. CAR Training: Feed in labelled data Machine Learning CARGIRLDOG Testing: Request labels for new data
- 7. How does it learn? Machine Learning Everything Else (“Conventional” Machine Learning) Neural Networks (AKA Deep Learning) Support Vector Machines Random Forests Gradient Boosting Linear Classifiers Nearest Neighbour Classifiers …….. “Hand-crafted” Features
- 9. What features do humans use?
- 10. Penguin Mania
- 11. Are humans always the best?
- 12. Are humans always the best?
- 13. Deep Learning (Artificial) Neural networks with lots of hidden layers (deep) The network determines what features are useful Lost favour until around 2006-2012 - Large amounts of data online - GPU and distributed processing Source: Alexander Del Toro Barba https://www.linkedin.com/pulse/how-artificial-intelligence-revolutionizing-finance-del-toro-barba
- 14. Neural Networks: Auto-features! Dog Cat Penguin 0.1 0.2 0.7 Input Layer Hidden Layers Output Layer Dog Cat Penguin 0.0 0.0 1.0 TRUTH ERROR ERROR ERROR Neuron / Perceptron = Matrix of Weights TRAINING 0.3 0.4 0.3 Back-Propagation Update weights to minimize errors
- 15. Deep Neural Networks Simplest Neural Network Example: Input Hidden Layer 2 neurons x1 x2 x4 x3 w11 b1 b2 w12 w13 w14 w21 w22 w23 w24 x w1 w2 W1x + b1 W2x + b2 (w11)(x1) + (w12)(x2) + (w13)(x3) + (w14)(x4) + b1 (w21)(x1) + (w22)(x2) + (w23)(x3) + (w24)(x4) + b2 Output Layer Dog Cat
- 16. Deep Neural Networks Simplest Neural Network Example: Input Hidden Layer 2 neurons x1 x2 x4 x3 w11 b1 b2 w12 w13 w14 w21 w22 w23 w24 x w1 w2 f(W1x + b1) f(W2x + b2) f = activation function (non linearity) Output Layer Dog Cat
- 17. Deep Neural Networks Activation Functions e.g. Sigmoid No longer recommended ReLU (Rectified Linear Unit) Very popular
- 18. Deep Neural Networks Simplest Neural Network Example: Input Hidden Layer 2 neurons x1 x2 x4 x3 w11 b1 b2 w12 w13 w14 w21 w22 w23 w24 x w1 w2 f(W1x + b1) f(W2x + b2) f = activation function (non linearity) Output Layer Dog Cat Softmax Function n1 n2
- 19. Deep Neural Networks Simplest Neural Network Example: Input Hidden Layer 2 neurons x1 x2 x4 x3 w11 b1 b2 w12 w13 w14 w21 w22 w23 w24 x w1 w2 Output Layer Dog Cat Softmax Function TRAINING Dog Cat TRUTH ERROR ERROR Back-Propagation Update weights to minimize errors 1.0 0.0 n1 n2 f(W1x + b1) f(W2x + b2)
- 20. Deep Neural Networks Back-propagation: Choose weight changes which move us “downwards” in the loss function, L Gradient Descent: W11 W12 L Network error measured by Loss function, L (Cost function) ERROR
- 21. Deep Neural Networks Gradient Descent = basis for many more sophisticated optimization methods Optimizer = Method of updating weights based on Loss Adam, Adagrad, Adamax, RMSProp……. etc etc See also http://sebastianruder.com/optimizing-gradient-descent/index.html
- 22. Deep Neural Networks We have discussed Fully Connected Neural Networks
- 23. Deep Neural Networks 256 256 (Small) RGB image Fully connected model : •256 x 256 x 3 weights PER neuron in first hidden layer! •Flattening input to a vector loses information What about image analysis……?
- 24. By Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45659236 Convolutional Neural Networks Input layer (image) First Hidden layer (Num channels (features) = 5) CNN model : •Neurons arranged in blocks •Each neuron connects to a small region of the input (receptive field) •Neurons in same channel share same weights •Weight-sharing -> detection of similar features across the image
- 25. Source: http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution Convolutional Neural Networks
- 26. Convolutional Neural Networks Adapted from : http://benanne.github.io/images/architecture.png MaxPool: reduce dimensionality, prevent overfitting Could also add “dropout” layer to help with overfitting
- 27. # Code Snippet model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) Convolutional Neural Networks https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
- 28. Anything with patterns in data! Fraud Detection Risk Assessment Speech Recognition Text Analysis Targeted advertising ……….. Practical Applications
- 29. e.g. Manufacturing Photo Management Driverless Car Remote Sensing Medical Images…… Imaging Applications
- 30. Image Categorisation flamingo Source : ImageNet 2011 (Large Scale Visual Recognition Challenge) Training data: 1.2 million images 1000 image categories
- 31. Image Object Localisation Source : ImageNet 2013 (Large Scale Visual Recognition Challenge) Training data: 396,000 images 200 object categories
- 32. Image Captioning "a man is throwing a frisbee in a park“ Source : http://cs.stanford.edu/people/karpathy/neuraltalk2/demo.html
- 33. Image Semantic Segmentation Source : www.robots.ox.ac.uk/~szheng/CRFasRNN.html
- 36. Medical Images – Brain MRI 3D Brain
- 37. The Real 3D Brain
- 38. Medical Images - Why AI? Radiologist • Error-prone • Subjective • Qualitative Computers • Tireless • Objective (repeatable results) • Quantitative
- 39. Deep Learning for Medical Images Litjens et al “A Survey on Deep Learning in Medical Image Analysis”, 2017
- 40. Deep Learning for Medical Images Litjens et al “A Survey on Deep Learning in Medical Image Analysis”, 2017
- 41. Data Science Bowl 2017
- 42. Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study Azizi et al “Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study”, 2016 Malignancy determination in Prostate Ultrasound Medical Imaging Applications
- 43. Detection of Tuberculosis in Chest X-Ray Kim et al “Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation”, 2016 Medical Imaging Applications
- 44. Gao et al “Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Detection”, 2016 Detecting Patterns of Interstitial Lung Disease (CT) Medical Imaging Applications
- 45. Ghafoorian et al “Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities”, 2016 Segmentation of White-Matter Hyperintensities (MRI) Medical Imaging Applications
- 46. INFANT Research INFANT Perinatal Research NeonatesPregnancy e.g. Diagnostic Testing Improved Monitoring Newborn Health Monitoring Nutrition Brain InjuryBrain Injury www.infantcentre.i
- 47. Hypoxic Ischemic Encephalopathy Oxygen Deprivation during Birth Cause of brain injury 2-5 cases per 1000 live births Wide range of severities and outcomes Which part of the brain is injured and how severely?Which part of the brain is injured and how severely?
- 48. Hypoxic Ischemic Encephalopathy Day 3-4 MRI acquired Diffusion-Weighted T2 (Anatomical)
- 49. Baby 1: Diffusion-Weighted MRI
- 50. Baby 2: Diffusion-Weighted MRI
- 51. Baby 3: Diffusion-Weighted MRI
- 52. The Neural Network (25 Subjects – per pixel Classification – round-robin 5 subjects training) Fully Convolutional Network with dilated convolutions Trained on image patches with Data Augmentation 3 x Hidden Convolutional Layers (32 features, 64 features, 96 features) Loss function : Binary Cross-entropy Optimizer : Adam
- 53. Brain Tissue Segmentation Segment 8 tissue types in anatomical scans NeoBrainS12 Public Challenge 2 x subjects fully labelled (training) 5 x subjects no labels (test)
- 54. Brain Tissue Segmentation Human SegmentationTraining Data
- 55. Network SegmentationTest Data Brain Tissue Segmentation
- 56. The Neural Network (2 training Subjects – per pixel classification) Fully Convolutional Network with dilated convolutions Trained on image patches with Data Augmentation Deep residual network with 11 convolutional layers stacked with batch- normalization and ‘ReLU’ activation layers. Loss function : Categorical Cross-entropy Optimizer : Adam
- 57. Getting Started with Deep Learning Preferably with GPU e.g. NVIDIA GTX see also http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/ Coding Frameworks: Caffe (Berkeley AI) Theano (University of Montreal) Tensorflow (Google) PyTorch (or Torch) Higher Level: Lasagne (layered on Theano) Keras (layered on Tensorflow/Theano) Also check out: Deep learning for JVM (Java, Scala, Hadoop, Spark) https://deeplearning4j.org/ Packages in e.g. Matlab & R (extensive list: http://deeplearning.net/software_links/)
- 58. Acknowledgements Kevin McGuinness, INSIGHT centre DCU Joseph Antony, INSIGHT centre DCU
- 59. http://cs231n.github.io/ http://neuralnetworksanddeeplearning.com http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial https://ayearofai.com https://arxiv.org/ (for research literature) https://github.com/kailashahirwar/cheatsheets-ai (cheat-sheets for programming) https://aiexperiments.withgoogle.com (fun stuff with neural nets) Useful (and fun) resources
Editor's Notes
- Vector notation, dot product
- Sigmoids saturate gradients during back-propagation ReLU is fast to compute, works well in many cases, caution with learning rate as incorrect settings can lead to dead neurons which always output 0.
- Want to convert our output values from the hidden layer into categorical probability.
- L shown as a function of weights
- Regular Neural Nets don’t scale well to full images. In CIFAR-10, images are only of size 32x32x3 (32 wide, 32 high, 3 color channels), so a single fully-connected neuron in a first hidden layer of a regular Neural Network would have 32*32*3 = 3072 weights. This amount still seems manageable, but clearly this fully-connected structure does not scale to larger images. For example, an image of more respectable size, e.g. 200x200x3, would lead to neurons that have 200*200*3 = 120,000 weights. Moreover, we would almost certainly want to have several such neurons, so the parameters would add up quickly! Clearly, this full connectivity is wasteful and the huge number of parameters would quickly lead to overfitting. the neurons in a layer will only be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner
- Flattening images to vectors loses information
- Each neuron connects to a small region of the input (receptive field) Weight-sharing -> detection of similar features across the image
- Central pixel replaced with a weighted sum of itself and surrounding pixels
- Max pooling, reduces dimensionality, helps overfitting Dropout – disable some neurons on occasion, so that the others learn to function better independently - helps overfitting
- Discuss modalities, pros/cons?
- Anisotropy Grayscale Lack of annotations
- Imaging protocols which ten years ago might have generated 50 images, may now produce thousands of images, all requiring expert examination Error-prone – tired, distracted, bad day, inexperienced Subjective – lots of disagreement! Qualitative – “quite big”, “fairly severe” (2D line measures) Tireless – Can work 24/7 Objective - Repeatable results, 100% agreement Quantitative – “10% bigger than average”
- common finding on brain MR images of patients diagnosed with small vessel disease (SVD) [1], multiple sclerosis [2], Parkinsonism [3], stroke [4], Alzheimer’s disease [5] and Dementia [6]. Associated with various measures of decline. Not well understood.
- use primitive functions that NVIDIA has specifically developed for deep learning on GPUs, called cuDNN Torch based on Lua language