Neural networks research in Medicine - Abstracts
SABBATINI%ccvax.unicamp.br@BITNET.CC.CMU.EDU
SABBATINI%ccvax.unicamp.br at BITNET.CC.CMU.EDU
Wed Aug 19 00:13:00 EDT 1992
ARTIFICIAL NEURAL NETWORKS IN MEDICINE AND BIOLOGY
Center for Biomedical Informatics
State University of Campinas, Campinas - Brazil
Abstracts of published work by the Center
Status of Aug 15 1992
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A HIGH-LEVEL LANGUAGE AND MICROCOMPUTER PROGRAM FOR
THE DESCRIPTION AND SIMULATION OF NEURAL ARCHITECTURES
Sabbatini, RME and Arruda-Botelho, AG
Center of Biomedical Informatics, Neurosciences
Applications Group, State University of Campinas, Campinas,
SP, BRAZIL)
The description, representation and simulation of complex
neural network structures by means of computers is an
essential step in the investigation of model systems and
inventions in the growing field of biological information
processing and neurocomputing. The handcrafting of neural
net architectures, however, is a long, tedious, difficult
and error-prone process, which can be substituted
satisfactorily by the neural network analogue of a computer
program or formal symbolic language. Several attempts have
been made to develop and apply such languages: P3, Hecht-
Nielsen's AXON, and Rochester's ISCON are some recent
examples.
We present here a new tool for the formal description and
simulation of artificial neural tissues in microcomputers.
It is a network editor and simulator, called NEUROED, as
well as a compiler for NEUROL, a high-level symbolic,
structured language which allows the definition of the
following elements of a neural tissue: a) elementary neural
architectonic units: each unit has the same number of cells
and the same internal interconnecting pattern and cell
functional parameters; b) elementary cell types: each cell
can be defined in terms of its basic functional parameters;
synoptic interconnections inside an architectonic unit
(axonic delay, weights and signal can be defined for each);
a cell can fan out to several others, with the same synoptic
properties; c) synaptic interconnections among units; d)
cell types and architectonic units can be replicated
automatically across neural tissue and interconnected; e)
cell types and architectonic units can be named and arranged
in hierarchical frames (parameter inheritance).
NEUROED's underlying model processing element (PE) is a
simplified Hodgkin-Huxley neuron, with RC-model, temporal-
summation, passive electrotonic potentials at dendritic
level, and a step transfer function with threshold level, a
fixed-size, fixed-duration, fixed-form spike, and an
absolute refractory period. Inputs Iij (i=1...NI) synapses
for j-th neuron are weighted with Wij (i=1...NI), where Wij
0 is defined for a inhibitory synapse, Wij = 0 for an
inactive or non-existent synapse and Wij 0 for an ex-
citatory synapse. Outputs Okj (k=1...NO) can have axonic
propagation delays Dkj (a delay can be equal to zero).
Firing of neurons in a network follows diffusion process,
according to propagation delays; random fluctuations in
several processes can be simulated. Several learning
algorithms can be implemented explicitly with NEUROL; a
Hebbian synapse-strength reinforcement rule has specific
language support now.
NEUROED's basic specifications are: a) written in Turbo
BASIC 1.0 for IBM-PC compatible machines, with CGA
monochrome graphics display and optional numerical
coprocessor; b) capacity of 100 neurons and 10.000 synapses;
c) three neural tissue layers: input, processing and output.
d) real-time simulation of neural tissue dynamics, with
three display modes: oscilloscope mode (displays membrane
potentials along time for several cells simultaneously); map
mode (displays bidimensional architecture with individual
cells, showing when they fire) and Hinton diagram (displays
interconnecting matrix with individual synapses, showing
when they fire); e) Realtime, interactive modification of
net parameters; and f) capability for building procedures,
functions and model libraries, which reside as external disk
files. NEUROED and NEUROL are easy to learn and to use,
intuitive for neuroscientists, and lend themselves to
modeling neural tissue dynamics for teaching purposes. We
are currently developing a basic "library" of NEUROED models
to teach basic neurophysiology to medical students.
Implementations of NEUROED and for parallel hardware are
also under way.
(Presented at the Fourth Annual Meeting of the Brazilian
Federation of Biological Societies, Caxambu, MG, July 1991)
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A CASCADED NEURAL NETWORK MODEL FOR PROCESSING
2D TOMOGRAPHIC BRAIN IMAGES
Dourado SC and Sabbatini RME
Center for Biomedical Informatics, State
University of Campinas, P.O. Box 6005, 13081 Campinas,
So Paulo, Brazil.
Artificial neural networks (ANN) have demonstrated many
advantages and capabilities in applications involving the
processing of biomedical images and signals. Particularly in
the field of medical image processing, ANNs have been used
in several ways, such as in image filtering, scatter
correction, edge detection, segmentation, pattern and
texture classification, image reconstruction and alignment,
etc. The adaptive nature of ANNs (i.e., they are capable of
learning) and the possibility of implementing its function
using truly massive parallel processors and neural
integrated circuits, in the future; are strong arguments in
favor of investigating new architectures, algorithms and
applications for ANNs in Medicine.
In the present work, we are interested into designing a
prototype ANN which could be capable of processing serial
sections of the brain, obtained from CT or MRI tomographs.
The segmented, outlined images, representing internal brain
structures, both normal and abnormal, would then be used as
an input to a three-dimensional stereotaxic radiosurgery
planning software.
The ANN-based algorithm we have devised was initially
implemented as a software simulation in a microcomputer (PC
80386, with VGA color graphics and a 80387 mathematical
coprocessor). It is structured as a compound ANN, comprised
by three cascading sub-networks. The first one receives the
original digitized image, and is a one-layer, fully
interconnected ANN, with one processing element (PE) per
image pixel. The brain image is obtained from a General
Electric CT system, with 256 x 256 pixels and 256 gray
levels. The first ANN implements a MHF lateral inhibition
function, based on a convolution filter of variable
dimension (3 x 3 up to 9 x 9 PE's), and it is used to
iteratively enhance borders in the image. The PE
interconnection (i.e. convolution) function can be defined
by the user as a disk file containing a set of synaptic
weights, which is read by the program; thus allowing for
experimentation with different sets of coefficients and
sizes of the convolution window. In this layer, PE's have
synaptic weights varying from -1 to 1, and the step function
as its transfer function. Usually after 2 to 3 iterations,
the borders are completely formed and do not vary any more,
but are too thick (i.e., the trace width spans several
pixels). In order to thin out the borders, the output of the
MHF ANN layer is subsequently fed into a three-layer
perceptron, which was trained off-line using the
backpropagation algorithm to perform thinning on smaller
straight line segments. Finally, the thinned out image
obtained pixel-wise at the this ANN's output is fed into a
third network, also a three-layer perceptron trained off-
line using the backpropagation algorithm to complete small
gaps ocurring in the image contours. The final image, also
256 x 256 pixels with 2 levels of gray, is passed to the 3D
slice reconstruction program, implemented with conventional,
sequential algorithms. A fourth ANN perceptron previously
trained by back-propagation to recognize the gray histogram
signature of small groups of pixels in the original image
(such as bone, liquor, gray and white matter, blood, dense
tumor areas, etc.), is used to false-color the entire image
according to the classified thematic regions.
The cascaded, multilayer ANN thus implemented performs very
well in the overall task of obtaining automatically outlined
and segmented brain slices, for the purposes of 3D
reconstruction and surgical planning. Due to the complexity
of algorithms and to the size of the image, the time spent
by the computer we use is inordinately large, preventing a
practical application. We are now studying the
implementation of this ANN paradigm in RISC-based and
vector-processing CPUs, as well as the potential
applications of neurochip prototyping kits already available
in the market.
(Presented at the I Latinoamerican Congress on Health
Informatics, Habana, Cuba, February 1992)
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COMPUTER SIMULATION OF A QUANTITATIVE MODEL FOR
REFLEX EPILEPSY
R.M.E. Sabbatini
Center of Biomedical Informatics and School of
Medicine of the State University of Campinas, Brazil.
In the present study we propose a continuous, lumped-
parameter, non-linear mathematical model for explaining the
quantitatively observed characteristics of a class of
experimental reflex epilepsy, namely audiogenic seizures in
rodents, and simulate this model with a especially contrived
microcomputer program. In a first phase of the study, we
have individually stimulated 280 adult Wistar albino rats
with a 112 dB white-noise sound source, and recorded the
latency, duration and intensity values of the psychomotor
components of the audiogenic reaction: after an initial
delay one or more circular running phases usually occurs,
followed or not by complete tonic-clonic seizures. In the
second step, we performed several multivariate statistical
analyses of these data, which have revealed many properties
of the underlying neural system responsible for the crisis;
such as the independence of the running and convulsive
phases; and a scale of severity which is correlated to the
value of latencies and intensities. Finally, a lumped-
parameter model based on a set of differential equations
which describes the macro behavior of the interaction of
four different populations of excitatory and inhibitory
neurons with different time constants and threshold elements
has been simulated in a computer, In this model, running
waves, which may occur several times before leading or not
to the final convulsive phase, are explained by the
oscillatory behavior of a controlling neural population,
caused by mixed feedback: an early, internal positive
feedback which results in the growing of excitation, and a
late negative feedback elicited by motor components of the
running itself, which causes the oscillation back to
inhibition. A second, threshold-triggered population
controls the convulsive phase and its subsequent refractory
phase. The results of the simulation have been found to
explain reasonably well the time course and structural
characteristics of the several forms of rodent audiogenic
epilepsy and correlates well with the existing knowledge
about the neural bases of this phenomenon.
(Presented at the Second IBRO/IMIA International Symposium
on Mathematical Approaches to Brain Functioning Diagnostics,
Prague, Czechoslovakia, September 1990).
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OUTCOME PREDICTION FOR CRITICAL PATIENTS UNDER INTENSIVE
CARE, USING BACKPROPAGATION NEURAL NETWORKS
P. Felipe Jr., R.M.E. Sabbatini, P.M. Carvalho-
Jnior, R.E. Beseggio, and R.G.G. Terzi
Center for Biomedical Informatics, State University of
Campinas, Campinas SP 13081-970 Brazil
Several scores have been designed to estimate death
probability for patients admitted to Intensive Care Units,
such as the APACHE and MPM systems, which are based on
regression analysis. In the present work, we have studied
the potential of a model of artificial neural network, the
three-layer perceptron with backpropagation learning rule,
to perform this task. Training and testing data were derived
from a Brazilian database which was previously used for
calculating APACHE scores. The neural networks were
trained with physiological, clinical and pathological data
(30 variables, such as worst pCO2, coma level, arterial
pressure, etc.) based on a sample of more than 300 patients,
whose outcome was known.
All networks were able to reach convergence with a small
global prediction error. Maximum percentages of 75% correct
predictions in the test dataset and 99.6 % in the training
dataset, were achieved. Maximum sensitivity and specificity
were 60% and 80%, respectively. We conclude that the neural
network approach has worked well for outcome prognosis in a
highly "noisy" dataset, with a similar, if slightly lower
performance than APACHE II, but with the advantage of
deriving its parameters from a regional dataset instead from
an universal model.
The paper will be presented at the MEDINFO'92 workshop on
"Applications of Connectionist System in Biomedicine",
September 8, 1992, in Geneva, Switzerland.
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Reprints/Preprints are available
Renato M.E. Sabbatini, PhD
Center for Biomedical Informatics
State University of Campinas
SABBATINI at CCVAX.UNICAMP.BR
SABBATINI at BRUC.BITNET
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