Analysis of uncertainty in different neural network structures using Monte Carlo Dropout

Abstract

Deep learning technologies developed at an exponential rate throughout the years. Starting from Convolutional Neural Networks (CNNs) to Involutional Neural Networks (INNs), there are several neural network (NN) architectures today, including Vision Transformers (ViT), Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs) etc. However, uncertainty cannot be represented in these architectures, which poses a significant difficulty for decision-making given that capturing the uncertainties of these state-of-the-art NN structures would aid in making specific judgments. Dropout is one method that may be implemented within Deep Learning (DL) networks as a technique to assess uncertainty. Dropout is applied at the inference phase to measure the uncertainty of these neural network models. This approach, commonly known as Monte Carlo Dropout (MCD), works well as a low-complexity estimation to compute uncertainty. MCD is a widely used approach to measure uncertainty in DL models, but majority of the earlier works focus on only a particular application. Furthermore, there are many state-of-the-art (SOTA) NNs that remain unexplored, with regards to that of uncertainty evaluation. Therefore an up-to-date roadmap and benchmark is required in this field of study. Our study revolved around a comprehensive analysis of the MCD approach for assessing model uncertainty in neural network models with a variety of datasets. Besides, we include SOTA NNs to explore the untouched models regarding uncertainty. In addition, we demonstrate how the model may perform better with less uncertainty by modifying NN topologies, which also reveals the causes of a model’s uncertainty. Using the results of our experiments and subsequent enhancements, we also discuss the various advantages and costs of using MCD in these NN designs. While working with reliable and robust models we propose two novel architectures, which provide outstanding performances in medical image diagnosis.