introduction to web-based machine learning with …...2018/01/17 · machine learning i march 8th...
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Introduction to Web-Based Machine Learning with DIGITS
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Machine Learning on Images
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Given examples, can we train a computer to do:
Source - http://cs231n.github.io/
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Machine Learning on Images
3 Source - http://cs231n.github.io/
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Artificial Neural Networks
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• Signal goes in, via input layer
• Weighted links transfer input values to neurons in hidden layers
• Signals are summed at hidden neurons and passed through transfer/activation function
• Processed signal arrives at output layer
• Decisions made using output signal(s)
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What’s in an (Artificial) Neuron?
5 Source - http://cs231n.github.io/
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Where’s the Knowledge?
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Weights encapsulate the knowledge of a network
Network learns using an algorithm that optimize weights given examples
Back propagation is commonly used – learn weights from examples usingsome linear algebra
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Classification Example – MNIST
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Which digit is it?
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Why Deep Learning?
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Type Classifier Distortion Preprocessing Error rate (%)
Linear classifier Pairwise linear classifier None Deskewing 7.6[9]
K-Nearest NeighborsK-NN with non-linear deformation (P2DHMDM)
None Shiftable edges 0.52[18]
Boosted StumpsProduct of stumps on Haar features
None Haar features 0.87[19]
Non-linear classifier 40 PCA + quadratic classifier None None 3.3[9]
Support vector machineVirtual SVM, deg-9 poly, 2-pixel jittered
None Deskewing 0.56[20]
Neural network 2-layer 784-800-10 None None 1.6[21]
Neural network 2-layer 784-800-10 elastic distortions None 0.7[21]
Deep neural network6-layer 784-2500-2000-1500-1000-500-10
elastic distortions None 0.35[22]
Convolutional neural network6-layer 784-40-80-500-1000-2000-10
None Expansion of the training data 0.31[15]
Convolutional neural network6-layer 784-50-100-500-1000-10-10
None Expansion of the training data 0.27[16]
Convolutional neural networkCommittee of 35 CNNs, 1-20-P-40-P-150-10
elastic distortions Width normalizations 0.23[8]
Convolutional neural networkCommittee of 5 CNNs, 6-layer 784-50-100-500-1000-10-10
None Expansion of the training data 0.21[17]
https://en.wikipedia.org/wiki/MNIST_database
Different Classifiers on the MNIST Database
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Why Deep Learning?
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More layers can encapsulate more knowledge
More weights to train – need more data, need more computation
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Convolutional Neural Networks
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Complex architectures, many layers – really good for image recognition tasks
Lots of computing power needed to do the training mathematics!
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GPUs to the Rescue!
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GPU cards are exceptionally well suited to Neural Network Mathematics
Orders of magnitude faster than CPU-based training
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How to Create and Train a Neural Network?
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The bad old days – (try to)write low-level performant, complex, numerical code
Source: http://www.cs.bham.ac.uk/~jxb/NN/nn.html
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How to Create and Train a Neural Network?
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Now many great frameworks – easier, but coding still needed (often Python)
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NVIDIA DIGITS
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• Web Based Platform
• Uses Popular Frameworks
• Define, Train, Test Networks
• Interact with Images
• Uses GPU Cards
DIGITS is a great platform if you want to work interactively, with no/minimal coding
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Launch via the portal – BioHPC OnDemand
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Start a WebDIGITS session, and then connect using link provided in your browser
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Using DIGITS
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1. Create a Dataset
2. Create and Train a Model
3. Run Predictions
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Demo 1 – MNIST Classification
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• Handwritten digits
• 52,500 Images for training
• We will use the LeNet modelA Pioneering Convolutional Neural Network
Follow tutorial at:
https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStarted.md
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Segmentation Example – Sunnybrook Cardiac Data
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Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. "Evaluation Framework for
Algorithms Segmenting Short Axis Cardiac MRI." The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070
MRI images in DICOM format. Find the left ventricle
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Segmentation Example – Sunnybrook Cardiac Data
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Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. "Evaluation Framework for
Algorithms Segmenting Short Axis Cardiac MRI." The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070
MRI images in DICOM format. Find the left ventricle
There it is – or so an expert tells us!
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Segmentation Example – Sunnybrook Cardiac Data
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Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. "Evaluation Framework for
Algorithms Segmenting Short Axis Cardiac MRI." The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070
MRI images in DICOM format. Find the left ventricle
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Demo 2 – Sunnybrook Cardiac Data
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Bit more complicated than Demo 1
• Need to use a plugin that reads DICOM and the expert ventricle contours
• Need to use a DICE loss functionUnbalanced classes
• Will fine tune a pre-trained FCN-8s networkFully Convolutional Networks for Semantic SegmentationLong et. al. 2015
Refer to:
https://github.com/NVIDIA/DIGITS/blob/master/examples/medical-imaging/README.md
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Web Resources
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Machine/Deep learning – Stanford CS231n: Convolutional Neural Networks for Visual Recognition.
http://cs231n.github.io/
DIGITS Documentationhttps://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStarted.md
Berkeley FCN Segmentation Modelshttp://fcn.berkeleyvision.org/
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Nano Courses
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Machine Learning / Deep Learning is a big topicBICF Nano Courses will run in 2018
Machine Learning IMarch 8th & 9th, 2018
Are you interested in machine learning? This course is an introductory course for students to learn the basics. Programming experience in Python is mandatory.
Course Size: 15 studentsAcademic Credit: 1 credit hour special topics
https://bicf.pages.biohpc.swmed.edu/bicf_nanocourses/#machine-learning-i