scholarly journals Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Turgay Ayer ◽  
Qiushi Chen ◽  
Elizabeth S. Burnside
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Disease Risk ◽  
Effective Means ◽  
Screening Mammography ◽  
Future Research ◽  
Diagnostic Decision ◽  
Artificial Neural ◽  
Diagnostic Decision Making

Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.

Forecasting Stock Returns with Artificial Neural Networks

2011 ◽  
pp. 47-79 ◽  
Author(s):  
Suraphan Thawornwong ◽  
David Enke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Returns ◽  
Future Research ◽  
Network Architectures ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

Artificial Neural Networks for Decision-Making in Urologic Oncology

2003 ◽  
Vol 43 (6) ◽  
pp. 596-603 ◽  
Author(s):  
Theodore Anagnostou ◽  
Mesut Remzi ◽  
Michael Lykourinas ◽  
Bob Djavan
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Urologic Oncology ◽  
Artificial Neural

scholarly journals Computational Principles of Supervised Learning in the Cerebellum

2018 ◽  
Vol 41 (1) ◽  
pp. 233-253 ◽  
Author(s):  
Jennifer L. Raymond ◽  
Javier F. Medina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Supervised Learning ◽  
Brain Area ◽  
Future Research ◽  
Brain Areas ◽  
Adaptive Mechanisms ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Organizational Principles

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

Sign in / Sign up

Export Citation Format

Share Document