Robert Dadashi-Tazehozi. rd2669. Deep Learning for Computer Vision and Natural Language Processing EECS 6894
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1 methods for Department of Computer Science Columbia University Deep Learning for Computer Vision and Natural Language Processing EECS 6894 methods for
2 Outline methods for
3 Outline methods for
4 Deep Learning for Cerebellar Ataxia and Functional Score Regression Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah H. Ying, and Jerry L. Prince Johns Hopkins University Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation Youngjin Yoo, Tom Brosch, Anthony Traboulsee, David K.B. Li, and Roger Tam University of British Columbia methods for
5 Outline methods for
6 data Deep Learning + Machine Learning Different goals and barriers while same data type, hence a comparative study - Data description - - Methods and Algorithms used - Project: Diabetic Retinopathy Detection Personal reasons methods for
7 Outline methods for
8 Ataxia methods for Overview A neuro-degenerative disease - Affects the cerebellum - Symptoms: lack of muscular coordination Goals: Classify different types of Ataxia: HC, SCA2, SCA6, AT Quantify functional loss based on structural change
9 Multiple sclerosis methods for Overview A inflammatory disease - Brain cells damaged: Demyelination - Symptoms: Mental, Physical, Psychatric troubles - Cause: Genetic? Environmental factors? Goals: Automatic segmentation of lesionned areas
10 Outline methods for
11 imaging methods for Human body composed of small magnets Magnets aligned then excited by pulses Magnets go back to their lowest energy state, electromagnetic waves emitted Processing of these waves enable to reconstruct 3D structure, differentiate muscles tissues from fat, white matter from grey matter in the brain
12 imaging methods for Figure: Initial state Figure: Excitation Figure: Energy emission
13 Outline methods for
14 Ataxia 168 scans 3D Images projected on 9 plans (Resulting in 32 * 32 images) Not enough labelled data Interest of generating synthetic data Multiple Sclerosis 1450 scans Resolution 256 * 256 * 50 Only 100 scans segmented Lot of unlabelled data Interest of unsupervised methods methods for
15 Outline methods for
16 Ataxia 3D Images projected on 9 plans (Resulting in 32 * 32 images) Generate translated and rotated images Reduce dimensions using a Stacked Auto-Encoder for each plan Multiple Sclerosis Images of resolution 256 * 256 * 50 Patches 9 * 9 * 3 (low-scale features) Train Restricted Boltzmann Machines for feature extraction Patches 15 * 15 * 5 (high-scale features) Train Deep Belief Network for feature extraction methods for
17 Stacked Auto-Encoder methods for Target vector is the output Along the way, compression of the data Trained layer after layer (greedy)
18 Restricted Boltzman Machines Visible and Hidden Units methods for Energy Based Method E(v, h) = v T Wh a T v b T h P(v, h) = 1 Z e E(v,h)
19 Restricted Boltzman Machines methods for
20 Restricted Boltzman Machines methods for How to train RBM? [Hinton 2002] Generate hidden units from visible units Generate visible units from these very hidden units Update weight: W i,j = α(< v j, h i > input < v j, h i > generateddata )
21 Deep Belief Networks [Hinton 2009] methods for Stacked RBMs Greedy training
22 Outline methods for
23 Ataxia methods for Multiple Sclerosis
24 Random Forests Bagging Data Selecting Features Generate Decision Tree methods for Averaging all the decision trees Classifier
25 Outline methods for
26 Ataxia methods for Multiple Sclerosis Dice Similarity Measure: Q : Lesionned voxels (manually segmented) P : Lesionned voxels (automatically segmented) d = 2 Q P Q + P Weiss (state of the art) : mean 29, std 13 Method: mean 38, std 19
27 methods for Thank you for your attention!
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