Title :
Risk assessment of axillary lymph node metastases in early breast cancer patients using the maximum entropy network
Author :
Choong, Poh Lian ; deSilva, J.S. ; Dawkins, Hugh J S ; Robbins, Peter ; Harvey, Jennet M. ; Sterrett, Gregory F. ; Papadimitriou, John ; Attikiouzel, Yianni
Author_Institution :
Center for Intelligent Inf. Process. Syst., Western Australia Univ., Nedlands, WA, Australia
Abstract :
Describes an artificial neural network (ANN) architecture for constructing maximum entropy (MaxEnt) models based on discrete distributions. Entropy is maximized by a partition function method involving the use of Lagrange multipliers which is capable of implementation by an ANN architecture. The maximum entropy network (MaxEN), consists of a training module and a testing module of interconnected processing elements. The practical use of the MaxEN network is illustrated with an application in the clinical management of early breast cancer patients
Keywords :
entropy; physiological models; Lagrange multipliers; artificial neural network architecture; axillary lymph node metastases; early breast cancer patients; interconnected processing elements; maximum entropy network; partition function method; risk assessment; testing module; training module; Artificial neural networks; Breast cancer; Entropy; Lagrangian functions; Lymph nodes; Metastasis; Neural networks; Pathology; Probability distribution; Risk management;
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
DOI :
10.1109/SIPNN.1994.344853
