Handling the predictive uncertainty of convolutional neural network in medical image analysis: a review

Abstract

Certainty is a significant part of disease detection, involving various kinds of imaging and machine learning (ML) methodologies. More precisely than other ML methods, a convolutional neural network (CNN) can classify images. As its parameters are deterministic, it cannot indicate the level of uncertainty in its predictions. Predictions made by predetermined CNNs may yield inaccurate findings, and there is no evaluation of confidence in these results. These outcomes may have harmful effects and lack trustworthiness. Uncertainty quantification (UQ) is critical to evaluating confidence in prediction. The noise, illumination, segmentation, and edge issues common to medical images also impact pre-trained CNN algorithms and lead to uncertain outcomes. This review aims to investigate the main inherent uncertainty issue in CNN and what form of UQ method can be applied with CNN to the task of medical image classification. This research proposes a novel approach by combining the superior properties of the Bayesian approach and fusion methods to reduce the uncertainty in CNN models. This study concludes that, despite a number of unresolved technical and scientific issues, various types of fusion approaches have improved the clinical validity for diagnosing and analytical purposes, and it is a field of study that has the capacity to grow significantly in the years to come.