Applying Support Vector Learning to Stem Cells Classification
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Applying Support Vector Learning to Stem Cells Classification

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Applying Support Vector Learning to Stem Cells Classification Applying Support Vector Learning to Stem Cells Classification Presentation Transcript

  • Introduction Online Machine Learning The Application Discussion Applying Support Vector Learning to Stem Cells Classification Ofer M. Shir oshir@liacs.nl Natural Computing Group Leiden University LUMC, MCB Seminar, 25-09-2006 Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Application Discussion Outline 1 Introduction The Problem: Stem Cells Classification Nucleus Imaging 2 Online Machine Learning The Teacher-Learner Model Simple Perceptron The SVM Algorithm Images as Instances 3 The Application Applying Perceptron Applying SVM 4 Discussion Conclusions Prospects Take-Home Message Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Problem: Stem Cells Classification The Application Nucleus Imaging Discussion Outline 1 Introduction The Problem: Stem Cells Classification Nucleus Imaging 2 Online Machine Learning The Teacher-Learner Model Simple Perceptron The SVM Algorithm Images as Instances 3 The Application Applying Perceptron Applying SVM 4 Discussion Conclusions Prospects Take-Home Message Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Problem: Stem Cells Classification The Application Nucleus Imaging Discussion Biological Motivation The nuclear lamina envelops the nucleus. Intact lamina is vital for cell survival, knowckdown of lamin B results in lethal embryos in mice, and mutations in Lamin A cause premature aging syndromes in human. In human mesenchemyal stem cells (hMSCs) the lamina shows a round and flat shape after 3D reconstruction. In hMSCs undergoing cell death the lamina shape dramatically changed and precedes the wholemarks of apoptosis, such as nuclear breakdown and chromatin fragmentation. Soon after caspase-8 activation, which ultimately leads to cell death, intranuclear organization of the lamina are formed and the depth of the nucleus increased. Similar changes in lamina organization are found in hMSCs undergoing replicative senescence. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Problem: Stem Cells Classification The Application Nucleus Imaging Discussion Biological Motivation Thus, it is possible that changes in the spatial organization of the lamina are correlated with the functional state of the cell. The spatial organization of the lamina can be used as an early marker to sort between healthy and not-healthy cells, as changes in lamina organization are visible before changes in cell morphology are detected. Here we tested this hypothesis using a machine learning approach. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Problem: Stem Cells Classification The Application Nucleus Imaging Discussion Nucleus Imaging The lamina of hMSCs was detected after transduction of the Lamin B-GFP lentivirus vector. Image stacks of the lamin B-GFP were aquired with a confocal microscope, and 3D reconstruction was obtained with TeloView. In control cells the XY and the XZ orientations revealed a round and flat shape of the lamina. After activation of caspase-8, the shape of the lamina is significantly changed. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Problem: Stem Cells Classification The Application Nucleus Imaging Discussion Control vs. Apoptotic Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning The Problem: Stem Cells Classification The Application Nucleus Imaging Discussion Nucleus Imaging Serial slicing along the XZ axis taken from an individual nucleus with DIPimage toolbox revealed little changes in the spatial organization of the lamina in a control cell. High variations were found in serial slicing taken from an apoptotic cell. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Outline 1 Introduction The Problem: Stem Cells Classification Nucleus Imaging 2 Online Machine Learning The Teacher-Learner Model Simple Perceptron The SVM Algorithm Images as Instances 3 The Application Applying Perceptron Applying SVM 4 Discussion Conclusions Prospects Take-Home Message Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Machine Learning: TRAINING Online learning considers a situation in which instances are presented one at a time, where the learner’s task is to learn a hypothesis which classifies the data correctly. Training phase: instances {xi }l in Rn , and their labels i=1 set Y = {−1, +1} are presented to the machine. The algorithm aims to update its hypothesis h : Rn → {±1} in order to minimize the prediction error. Various algorithms have different update rules. Analogy: teacher, learner, corrections. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Machine Learning: TESTING This training phase is followed by the testing phase, where more data is given to the learned hypothesis. Ideally unseen data. (Why...?) The correct labels are not presented to the machine! The accuracy rate is considered - how did the machine perform? Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Simple Perceptron The Perceptron algorithm (Rosenblatt, 1957) is an online learning algorithm for finding a consistent hypothesis within the class of hyperplanes: C = h(x) = sign wT · x + b w t ∈ Rn , b ∈ R The optimal hyperplane is defined as the one with the maximal margin of separation between the two instances classes. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Perceptron: Optimal Hyperplane Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Non-Realizable for Hyperplanes Separation But what if the data is not linearly-separable...? There is no hyperplane separator hypothesis for the problem! Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Mapping... We would like then to map the instances to a higher dimensional space, where linear separation is feasible: Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Desirable Mapping Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances The Algorithm The Support Vector Machines (SVM) algorithm (Boser, Guyon and Vapnik, 1992) is a linear method in a high-dimensional feature space, which is non-linearly interlinked to the instance space. It allows learning a hypothesis for data which is not linearly-separable. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances The Kernel Function The function φ : Rn → F maps the instance vectors onto a higher dimensional space F, and then the SVM aims to find a hyperplane separator with the maximal margin in this space. k (xi , xj ) ≡ φ(xi )T φ(xj ) Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Kernels In particular, we consider the following kernel functions: The polynomial kernel: d k (xi , xj ) = γ xT · xj + r i (1) Radial basis function (RBF) kernel: 1 2 k (xi , xj ) = exp − xi − xj (2) 2σ 2 The sigmoid kernel: k (xi , xj ) = tanh κ xT · xj + Θ i (3) Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Images as Instances Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Grayscale Images Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction The Teacher-Learner Model Online Machine Learning Simple Perceptron The Application The SVM Algorithm Discussion Images as Instances Intermediate Conclusions Grayscale images are simply matrices with normalized elements in [0, 1]. In particular, as instance vectors in Rn ! Essentially, an image could be introduced directly to the learning algorithm, without further processing. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning Applying Perceptron The Application Applying SVM Discussion Outline 1 Introduction The Problem: Stem Cells Classification Nucleus Imaging 2 Online Machine Learning The Teacher-Learner Model Simple Perceptron The SVM Algorithm Images as Instances 3 The Application Applying Perceptron Applying SVM 4 Discussion Conclusions Prospects Take-Home Message Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning Applying Perceptron The Application Applying SVM Discussion Experimental Procedure: Modus Operandi Training phase: provide the machine with shuffled 2000 slices and their correct labels. Testing phase: test the machine with shuffled 1040 slices without their labels - and check its accuracy. Correct classification means that the output of the machine per given instance is its correct label as in our database. Wrong classification (error rate) - vice versa. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning Applying Perceptron The Application Applying SVM Discussion Applying Perceptron Applying the Perceptron was straightforward, with respect to parameter settings, and did not require any preliminary tuning. However, the algorithm obtained, after training, a test accuracy of 70.38% (732/1040 images were classified correctly). This result led us to the conclusion that the data was not linearly-separable, and a stronger approach was much needed. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning Applying Perceptron The Application Applying SVM Discussion Applying SVM - Preliminary Applying SVM (libsvm package) to the classification problem with default settings yielded test accuracy of 55% on average. Thus, tuning the kernel parameters was essential - several parameters as well as the profile of the kernel (Eq. 1, 2, 3) and its various appropriate parameters ({γ, r, d}, {σ} and {κ, Θ}). The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [Hansen et al., 2001] was selected as the optimization tool: the cross-validation accuracy rate was the objective function to be optimized. Each objective function evaluation takes 11 minutes on a single processor: runs were limited. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Online Machine Learning Applying Perceptron The Application Applying SVM Discussion SVM - Numerical Results CMA-ES found an RBF kernel with 98.90% cross-validation. Testing phase: Accuracy of 97.02% - 1009/1040 images were classified correctly! Highly satisfying! Beyond any expectation! Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Conclusions Online Machine Learning Prospects The Application Take-Home Message Discussion Outline 1 Introduction The Problem: Stem Cells Classification Nucleus Imaging 2 Online Machine Learning The Teacher-Learner Model Simple Perceptron The SVM Algorithm Images as Instances 3 The Application Applying Perceptron Applying SVM 4 Discussion Conclusions Prospects Take-Home Message Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Conclusions Online Machine Learning Prospects The Application Take-Home Message Discussion Conclusions Machine learning as a way of life. Machine classification of stem cells is feasible! Numerical results are remarkably excellent. No further image analysis, after the image acquisition, is required. Behind everything in life there is a matrix... Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Conclusions Online Machine Learning Prospects The Application Take-Home Message Discussion Prospects Classification of other ”colors”. Classification of 3D images! Analysis of time-dependent 3D movies. Ofer M. Shir SVM to Stem-Cells Classification
  • Introduction Conclusions Online Machine Learning Prospects The Application Take-Home Message Discussion Take-Home Message Natural computing, machine learning and data mining are rich fields with a lot to offer! Find yourself a nice computer-scientist, and invest in your relationship. You may prefer to consider those tools as a black-boxes. BUT then apply and boost medicine... Ofer M. Shir SVM to Stem-Cells Classification