PRE-PRINT AVAILABILITY.

[P.Refenes@cs.ucl.ac.uk]

The following pre-print (SPIE-90, Boston, Nov. 5-9 1990) is available.
(write or e-mail to A. N. Refenes at UCL)

AN INTEGRATED NEURAL NETWORK SYSTEM for HISTOLOGICAL IMAGE UNDERSTANDING


         A. N. REFENES, N. JAIN & M. M. ALSULAIMAN
              Department of Computer Science,
                 University College London,
                  Gower Street, WC1, 6BT,
                        London, UK.

ABSTRACT


This  paper  describes  a  neural  network   system   whose
architecture   was   designed   so   that  it  enables  the
integration of heterogeneous  sub-networks  for  performing
specialised tasks. Two types of networks are integrated: a)
a low-level feature  extraction  network  for  sub-symbolic
computation,  and  b)  a  high-level  network  for decision
support.

The  paper  describes  a  non  trivial   application   from
histopathology, and its implementation using the Integrated
Neural Network System. We show that  with  careful  network
design,   the  backpropagation  learning  procedure  is  an
effective way of training neural networks for  histological
image  understanding.  We evaluate the use of symmetric and
asymmetric squashing functions in  the  learning  procedure
and  show that symmetric functions yield faster convergence
and 100% generalisation performance.