Download Session: Neural Network Approach as an Aid in

Session: Neural Network Approach as an Aid in Medical Diagnosis
Talk: Introduction to Neural Networks in Medical Diagnosis.
Modern medical diagnostic equipment provides large amounts of data in form of numbers,
graphical plots and images, making the clinical interpretation of such data quite complex task.
“Computational intelligence” or “soft computing” methods are helpful in non-linear data
analysis. They include artificial neural networks (ANNs), fuzzy and neurofuzzy systems,
pattern recognition, multidimensional visualization and many other useful additions to
standard statistical techniques. These methods support medical diagnosis in a variety of ways:
selecting subsets of relevant independent variables, reducing dimensionality of the data,
handling noise in the data, using symbolic and fuzzy information together with numerical
values, performing classification and calculating probabilities of different diagnoses,
including uncertainty of the measured data, detecting outliers, creating predictive models,
discovering knowledge in form of decision trees or logical rules (crisp or fuzzy), stabilizing
and improving accuracy of predictions by using committees of models, predicting missing
values, and visualizing important relationships among data samples.
Artificial neural networks have been used with success in many medical applications, from
dentistry to psychiatry. Some applications have proven to be superior over human diagnosis
and are used in clinical settings. Neural networks received wide publicity after their
successful deployment to diagnose heart attacks in emergency rooms. More applications of
ANNs in cardiology, oncology and other fields are currently undergoing clinical tests, while
many applications are still at the research level. Interpretations of biomedical signals, such as
electrocardiograms, electroencephalograms and medical images, done with ANNs and related
computational intelligence techniques, promise many new clinical applications.
This talk explains basic neural network concepts in non-technical terms, and presents
examples of their applications in medical diagnosis. ANNs are collections of simple
processing elements (called “neurons”), each combining in-coming input values, processing
them and sending signals to other neurons. Learning in such networks is based on adaptation
of network parameters leading to association of inputs (independent variables) with outputs
(dependent variables). In supervised learning a cost function that measures the difference
between achieved and desired outputs is minimized for all training samples. Results of
medical tests or features of biomedical signals may be associated with various diseases in this
way. Logistic regression, well-known statistical technique, corresponds to specific learning
procedure for a single neural element that uses logistic processing function. Automatic
selection of features relevant to diagnosis simplifies networks and makes them more accurate.
After training, ANNs are able to make reliable estimations of probabilities of different
diagnoses in novel situations.
Contrary to the widespread opinion that neural networks are ‘black boxes’, producing
incomprehensible results, efficient neural methods for extraction of knowledge in form of
logical rules exist, solving the knowledge acquisition bottleneck problem for building medical
expert systems. Examples of interesting knowledge discovered in medical databases in this
way are given. ANNs can discover inconsistencies and artifacts in the data. For example,
healthy/sick labeling of samples creates unrealistic, sharp decision borders that may be
discovered by extraction of logical rules. In some cases such analysis shows that the data
contains only trivial knowledge.
Short biography:
Wlodzislaw Duch is a professor of theoretical physics and informatics, currently heading the
Department of Informatics at Nicolaus Copernicus University, Torun, Poland. He holds
habilitation degree (D.Sc.) in many body physics (1987) and Ph.D. in quantum chemistry
(1980). He has held a number of academic positions at universities and scientific institutions
all over the world. These include University of Southern California in Los Angeles and the
University of Florida in Gainesville, USA; University of Alberta in Edmonton, Canada; Meiji
University, Kyushu Institute of Technology and Rikkyo University in Japan; Louis Pasteur
Universite in Strasbourg, France; Max-Planck-Institut für Astrophysik in Germany (every
year since 1984) to name only a few.
He has been an editor of a number of professional journals, including IEEE Transactions on
Neural Networks, Computer Physics Communications, and a head of the scientific committee
of the Polish Cognitive Science journal. He worked as an expert for the European Union 5th
Framework Science Program, Polish Committee of Scientific Research and the Ministry of
Education. He has published 4 books and over 250 scientific and popular articles in many
journals. He has been awarded a number of grants by Polish state agencies, foreign
committees as well as European Union institutions.
His full CV is at: www.phys.uni.torun.pl/~duch.