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/

Machine Learning on Images

3 Source - http://cs231n.github.io/

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)

What’s in an (Artificial) Neuron?

5 Source - http://cs231n.github.io/

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

Classification Example – MNIST

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Which digit is it?

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

Why Deep Learning?

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More layers can encapsulate more knowledge

More weights to train – need more data, need more computation

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!

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

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

How to Create and Train a Neural Network?

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Now many great frameworks – easier, but coding still needed (often Python)

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

Launch via the portal – BioHPC OnDemand

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Start a WebDIGITS session, and then connect using link provided in your browser

Using DIGITS

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1. Create a Dataset

2. Create and Train a Model

3. Run Predictions

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

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

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!

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

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

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/

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