Talk presented at WSOM 2016 in Houston/Texas. Machine learning based classification of FDG-PET scan data for the diagnosis of neurodegenerative disorders

1. Classification of FDG-PET* Brain Data
* Fluorodeoxyglucose positron emission tomography
Deborah Mudali 1,* Michael Biehl 1
Klaus L. Leenders 2 Jos B.T.M. Roerdink 1,3
1 Johann Bernoulli Institute for Mathematics
and Computer Science, University of Groningen, NL
2 Department of Neurology
University Medical Center Groningen, NL
3 Neuroimaging Center
University Medical Center Groningen, NL
* Mbarara University of Science & Technology, Uganda

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overview
Example application
Classification of Parkinsonian Syndromes
based on FDG-PET brain data
Combination: PCA + GMLVQ
comparison with DT, SVM
Conclusion and Outlook
Prototype-based classification
Learning Vector Quantization
Generalized Matrix Relevance Learning (GMLVQ)

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∙ identification of prototype vectors from labeled example data
∙ (dis)-similarity based classification (e.g. Euclidean distance)
Learning Vector Quantization
N-dimensional data, feature vectors
• initialize prototype vectors
for different classes
competitive learning: Winner-Takes-All LVQ1 [Kohonen, 1990, 1997]
• identify the winner
(closest prototype)
• present a single example
• move the winner
- closer towards the data (same class)
- away from the data (different class)
feature space

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prototype based classifier
- represent data by one or
several prototypes per class
- classify a query according to the
label of the nearest prototype
(or alternative voting schemes)
- local decision boundaries according
to (e.g.) Euclidean distances
+ robustness to outliers, low storage needs and computational effort
- model selection: number of prototypes per class, etc.
feature space
?
? appropriate distance / (dis-) similarity measure
+ parameterization in feature space, interpretability

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fixed distance measures:
- choice based on prior knowledge or preprocessing
- determine prototypes from example data
by means of (iterative) learning schemes
e.g. heuristic LVQ1, cost function based Generalized LVQ
relevance learning, adaptive distances:
- employ parameterized distance measure
- update parameters in one training process with prototypes
- optimize adaptive, data driven dissimilarity
example: Matrix Relevance LVQ
Learning Vector Quantization

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Relevance Matrix LVQ
generalized quadratic distance in LVQ:
variants: global/local matrices (piecewise quadratic boundaries)
diagonal relevances (single feature weights)
rectangular (low-dim. representation)
[Schneider et al., 2009]
relevance matrix:
quantifies importance of features and pairs of features
summarizes relevance of feature j
( for equally scaled features )
training: optimize prototypes and Λ w.r.t. classification of examples

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cost function based training
one example: Generalized LVQ [Sato & Yamada, 1995]
sigmoidal (linear for small arguments), e.g.
E approximates number of misclassifications
linear
E favors large margin separation of classes, e.g.
two winning prototypes:
minimize
small , large
E favors class-typical prototypes

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cost function based LVQ
There is nothing objective about objective functions
James McClelland

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FDG-PET (Fluorodeoxyglucose positron emission tomography, 3d-images)
condition
Glucoseuptake
n=18 HC
Healhy controls
n= 20 PD
Parkinson’s Disease
n=21 MSA
Multiple System Atrophy
n=17 PSP
Progressive Supranuclear
Palsy
classification of FDG-PET data
[http://glimpsproject.com]

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work flow
subjects 1….P
voxels1….N(N≈200000)
SubjectResidualProfileSRP
log-transformed
high-intensityvoxels
GroupInvariant
Subprofile(GIS)
subjectsocres1….P
subjects 1….P
Scaled Subprofile Model PCA
based on a given group of subjects
SSMPCA
data and pre-processing:
D. Mudali, L.K. Teune, R. J. Renken, K. L. Leenders,
J. B. T. M. Roerdink. Computational and Mathematical
Methods in Medicine. March 2015, Art.ID 136921, 10p.
and refs. therein

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work flow
subjects 1….P
voxels1….N(N≈200000)
SubjectResidualProfileSRP
log-transformed
high-intensityvoxels
GroupInvariant
Subprofile(GIS)
subjectsocres1….P
subjects 1….P
Scaled Subprofile Model PCA
based on a given group of subjects
applied to
novel subject
test
labels
(condition)
GMLVQ classifier
prototypes and distance
?
SSMPCA

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Healthy controls vs. Parkinson’s Disease
38 leave-one-out validation runs
averaged…
prototypes relevance matrix
ROC of leave-one-out
prediction
example: HC vs. PD
(w/o z-score transform.)

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Healthy controls vs. Progressive Supranuclear Palsy
35 leave-one-out validation runs,
averaged…
prototypes relevance matrix
example: HC vs. PSP
ROC of leave-one-out
prediction
(w/o z-score transform.)

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GMLVQ
NPC
accuracies
Note:
maximum margin perceptron - aka SVM with linear kernel - (Matlab svmtrain)
achieves performance similar to GMLVQ
performance comparison
Decision tree
(C4.5)
using all PC
Mudali et al.
2015

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four classes: HC / PD / MSA / PSP
leave-one-out confusion matrix for the four-class problem
GM
lin.
77.8 %
65.0 %
64.7 %
76.2 %
class acc.
66.7 %
60.0 %
52.9 %
89.0 %
class acc.(1 vs 1)

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HC / PD / MSA / PSP
HC
PSP
PD
MSA
GMLVQ
visualization of training
data set in terms of the
leading eigenvectors of Λ

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diseases only: PD / MSA / PSP
leave-one-out confusion matrix for the three-class problem
lin.(1 vs 1)

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diseases only: PD / MSA / PSP
MSA
PD
PSP
GMLVQ
visualization of training
data set in terms of the
leading eigenvectors of Λ

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discussion / conclusion
- detection and discrimination of Parkinsonian syndromes:
GMLVQ classifier and SVM clearly outperform decision trees
decision trees
- serious limitations:
small data set
leave-one-out validation
over-fitting
- accuracy is not enough:
can we obtain better insight into the classifiers ?

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outlook/work in progress
- optimization of the number of PCs used as features
shown to improve decision tree performance
potential improvement for other classifiers
- larger data sets
- understanding relevances in voxel-space
relevant PC hint at discriminative between-patient variability
PCA:
recent example:
diagnosis of rheumatoid arthritis based on cytokine expression
[L. Yeo et al., Ann. of the Rheumatic Diseases, 2015]

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http://matlabserver.cs.rug.nl/gmlvqweb/web/
Matlab code:
Relevance and Matrix adaptation in Learning Vector
Quantization (GRLVQ, GMLVQ and LiRaM LVQ):
http://www.cs.rug.nl/~biehl/
links
Pre- and re-prints etc.:
A no-nonsense beginners’ tool for GMLVQ:
http://www.cs.rug.nl/~biehl/gmlvq

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Questions ?