Transcript Title of Talk

Introduction to
Neural Networks
in Medical Diagnosis
Włodzisław Duch
PhD, DSc
Dept. of Informatics, Nicholas Copernicus
University, Poland
What is it about?
• Data is precious! But also overwhelming ...
• Statistical methods are important but new
techniques may frequently be more accurate
and give more insight into the data.
• Data analysis requires intelligence.
• Inspirations come from many sources,
including biology: artificial neural networks,
evolutionary computing, immune systems ...
Computational Intelligence
Pattern
Recognition
Fuzzy
logic
Expert
systems
Neural
networks
Evolutionary
algorithms
Computational Intelligence
Data + Knowledge
Artificial Intelligence
Visualization
Multivariate
statistics
Machine
learning
Probabilistic
methods
What do these methods do?
• Provide non-parametric models of data.
• Allow to classify new data to pre-defined
categories, supporting diagnosis & prognosis.
• Allow to discover new categories.
• Allow to understand the data, creating fuzzy
or crisp logical rules.
• Help to visualize multi-dimensional
relationships among data samples.
• Help to model real neural networks!
Neural networks
• Inspired by neurobiology: simple elements
cooperate changing internal parameters.
• Large field, dozens of different models, over
500 papers on NN in medicine each year.
• Supervised networks: heteroassociative
mapping X=>Y, symptoms => diseases,
universal approximators.
• Unsupervised networks: clusterization,
competitive learning, autoassociation.
• Reinforcement learning: modeling behavior,
playing games, sequential data.
Real and artificial neurons
Dendrites
Signals
Synapses
Nodes –
artificial
neurons
Synapses
(weights)
Axon
Neural network for MI diagnosis
~ p(MI|X) 0.7 Myocardial Infarction
Output
weights
Input
weights
Inputs:
-1
65
Sex
Age
1
5
3
1
Smoking Pain
Elevation
Pain
Intensity Duration ECG: ST
MI network function
Training: setting the values of weights and
thresholds, efficient algorithms exist.
Effect: non-linear regression function
 5 o  6 i

FMI  X     Wij   W jk X k  
 k 1

 i 1
Such networks are universal approximators:
they may learn any mapping X => Y
Knowledge from networks
Simplify networks: force most weights to 0,
quantize remaining parameters, be constructive!
• Regularization: mathematical technique
improving predictive abilities of the network.
• Result: MLP2LN neural networks that are
equivalent to logical rules.
Recurrence of breast cancer
Data from: Institute of Oncology, University
Medical Center, Ljubljana, Yugoslavia.
286 cases, 201 no recurrence (70.3%),
85 recurrence cases (29.7%)
no-recurrence-events, 40-49, premeno, 25-29,
0-2, ?, 2, left, right_low, yes
9 nominal features: age (9 bins), menopause,
tumor-size (12 bins), nodes involved (13 bins),
node-caps, degree-malignant (1,2,3), breast,
breast quad, radiation.
Recurrence of breast cancer
Data from: Institute of Oncology, University
Medical Center, Ljubljana, Yugoslavia.
Many systems used, 65-78% accuracy reported.
Single rule:
IF (nodes-involved  [0,2]  degree-malignant = 3
THEN recurrence, ELSE no-recurrence
76.2% accuracy, only trivial knowledge in the data:
Highly malignant breast cancer involving many
nodes is likely to strike back.
Recurrence - comparison.
Method
MLP2LN 1 rule
SSV DT stable rules
10xCV accuracy
76.2
75.7  1.0
k-NN, k=10, Canberra
74.1 1.2
MLP+backprop.
CART DT
FSM, Gaussian nodes
Naive Bayes
73.5  9.4 (Zarndt)
71.4  5.0 (Zarndt)
71.7  6.8
69.3  10.0 (Zarndt)
Other decision trees
< 70.0
Breast cancer diagnosis.
Data from University of Wisconsin Hospital,
Madison, collected by dr. W.H. Wolberg.
699 cases, 9 features quantized from 1 to 10:
clump thickness, uniformity of cell size, uniformity
of cell shape, marginal adhesion, single epithelial
cell size, bare nuclei, bland chromatin, normal
nucleoli, mitoses
Tasks: distinguish benign from malignant cases.
Breast cancer rules.
Data from University of Wisconsin Hospital,
Madison, collected by dr. W.H. Wolberg.
Simplest rule from MLP2LN, large regularization:
If
uniformity of cell size < 3
Then benign
Else malignant
Sensitivity=0.97, Specificity=0.85
More complex NN solutions, from 10CV estimate:
Sensitivity =0.98, Specificity=0.94
Breast cancer comparison.
Method
10xCV accuracy
k-NN, k=3, Manh
FSM, neurofuzzy
97.0  2.1 (GM)
96.9  1.4 (GM)
Fisher LDA
MLP+backprop.
LVQ
IncNet (neural)
Naive Bayes
SSV DT, 3 crisp rules
LDA (linear discriminant)
Various decision trees
96.8
96.7 (Ster, Dobnikar)
96.6 (Ster, Dobnikar)
96.4  2.1 (GM)
96.4
96.0  2.9 (GM)
96.0
93.5-95.6
Melanoma skin cancer

Collected in the Outpatient Center of
Dermatology in Rzeszów, Poland.

Four types of Melanoma: benign,
blue, suspicious, or malignant.

250 cases, with almost equal class distribution.

Each record in the database has 13 attributes:
asymmetry, border, color (6), diversity (5).

TDS (Total Dermatoscopy Score) - single index

Goal: hardware scanner for preliminary
diagnosis.
Melanoma results
Method
Rules Training %
Test %
MLP2LN, crisp rules
4
98.0 all
100
SSV Tree, crisp rules
4
97.5±0.3
100
FSM, rectangular f.
7
95.5±1.0
100
knn+ prototype selection
13
97.5±0.0
100
FSM, Gaussian f.
15
93.7±1.0
95±3.6
knn k=1, Manh, 2 features --
97.4±0.3
100
--
96.2
LERS, rough rules
21
Antibiotic activity of pyrimidine
compounds.
Pyrimidines: which compound
has stronger antibiotic activity?
Common template, substitutions
added at 3 positions, R3, R4 and R5.
27 features taken into account: polarity, size,
hydrogen-bond donor or acceptor, pi-donor or
acceptor, polarizability, sigma effect.
Pairs of chemicals, 54 features, are compared,
which one has higher activity?
2788 cases, 5-fold crossvalidation tests.
Antibiotic activity - results.
Pyrimidines: which compound
has stronger antibiotic activity?
Mean Spearman's rank correlation coefficient
used:
-1< rs < +1
Method
Rank correlation
FSM, 41 Gaussian rules
Golem (ILP)
Linear regression
CART (decision tree)
0.77±0.03
0.68
0.65
0.50
Thyroid screening.
Garavan Institute,
Sydney, Australia
15 binary, 6 continuous
Training: 93+191+3488
Validate: 73+177+3178

Determine important
clinical factors

Calculate prob. of
each diagnosis.
Clinical
findings
Age
sex
…
…
TSH
T4U
T3
TT4
TBG
Final
Hidden diagnoses
units
Normal
Hypothyroid
Hyperthyroid
Thyroid – some results.
Accuracy of diagnoses obtained with different systems.
Method
Rules/Features Training % Test %
MLP2LN optimized
4/6
99.9
99.36
CART/SSV Decision Trees
3/5
99.8
99.33
Best Backprop MLP
-/21
100
98.5
Naïve Bayes
-/-
97.0
96.1
k-nearest neighbors
-/-
-
93.8
Psychometry
MMPI (Minnesota Multiphasic Personality
Inventory) psychometric test.
Printed forms are scanned or computerized
version of the test is used.
• Raw data: 550 questions, ex:
I am getting tired quickly: Yes - Don’t know - No
• Results are combined into 10 clinical scales and
4 validity scales using fixed coefficients.
• Each scale measures tendencies towards
hypochondria, schizophrenia, psychopathic
deviations, depression, hysteria, paranoia etc.
Psychometry
• There is no simple correlation between single
values and final diagnosis.
• Results are displayed in form of a histogram,
called ‘a psychogram’. Interpretation depends
on the experience and skill of an expert, takes
into account correlations between peaks.
Goal: an expert system providing evaluation and
interpretation of MMPI tests at an expert level.
Problem: agreement between experts only 70% of
the time; alternative diagnosis and personality
changes over time are important.
Psychometric data
1600 cases for woman, same number for men.
27 classes:
norm, psychopathic, schizophrenia, paranoia,
neurosis, mania, simulation, alcoholism, drug
addiction, criminal tendencies, abnormal
behavior due to ...
Extraction of logical rules: 14 scales = features.
Define linguistic variables and use FSM,
MLP2LN, SSV - giving about 2-3 rules/class.
Psychometric data
Method
Data
N. rules Accuracy
+Gx%
C 4.5
♀
55
93.0
93.7
♂
61
92.5
93.1
♀
69
95.4
97.6
♂
98
95.9
96.9
FSM
10-CV for FSM is 82-85%, for C4.5 is 79-84%.
Input uncertainty +Gx around 1.5% (best ROC)
improves FSM results to 90-92%.
Psychometric Expert
Probabilities for different classes.
For greater uncertainties more
classes are predicted.
Fitting the rules to the conditions:
typically 3-5 conditions per rule,
Gaussian distributions around
measured values that fall into the
rule interval are shown in green.
Verbal interpretation of each
case, rule and scale dependent.
Visualization
Probability of classes versus
input uncertainty.
Detailed input probabilities
around the measured values
vs. change in the single scale;
changes over time define
‘patients trajectory’.
Interactive multidimensional
scaling: zooming on the new
case to inspect its similarity to
other cases.
Summary
Neural networks and other computational
intelligence methods are useful additions to the
multivariate statistical tools.
They support diagnosis, predictions, and data
understanding: extracting rules, prototypes.
FDA has approved many devices that use ANNs:
Oxford’s Instruments Ltd EEG analyzer,
Cardionetics (UK) ECG analyzer.
PAPNET (NSI), analysis of Pap smears
…
Challenges
Fully automatic universal data analysis systems:
press the button and wait for the truth …
•
•
•
•
Discovery of theories rather than data models
Integration with image/signal analysis
Integration with reasoning in complex domains
Combining expert systems with neural networks
….
We are slowly getting there.
More & more computational intelligence tools
(including our own) are available.