Training artificial neural networks (ANNs) with multiple target values to reduce output uncertainty

Abstract

We have shown previously that there is uncertainty associated with the output of artificial neural network (ANN) and we have now developed a new method to reduce this uncertainty by training ANNs with multiple target values. In conventional ANN training, binary target values are used to represent, e.g., benign and malignant cases. However, this method does not take into consideration the various histology subtypes. In this work, we used both simulated datasets and a mammography dataset to show that the conventional training method leads to larger uncertainty in the ANN output. Eight ANNs were trained by choosing different initial weights and ANN output variance was measured by the average standard deviation (SD) of the 8 ANNs' outputs for each test case. In the simulation, in addition to the conventional training method using binary target values, we also trained ANNs with multiple target values, and a set of continuous target values derived from a likelihood ratio of the underlying distributions. For the mammogram study, we assigned multiple target values based on histology subtypes. Both the simulation and mammogram studies showed that ANNs produce very close overall performance regardless the training methods. However, training neural networks with multiple target values demonstrated lower uncertainty in the ANN outputs.