Uncertainty CNNs: A path to enhanced medical image classification performance

The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient modeling techniques and enhanced data storage capabilities. However, methodologies that account for the uncertainty of predictions remain relatively uncommon in medical imaging. Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. In this paper, we introduce a low-complexity uncertainty-based CNN architecture for medical image classification, particularly focused on tumor and heart failure (HF) detection. The model's predictive (aleatoric) uncertainty is quantified through a test-set augmentation technique, which generates multiple surrogates of each test image. This process enables the construction of empirical distributions for each image, which allows for the calculation of mean estimates and credible intervals. Importantly, this methodology not only provides UQ, but also significantly improves the model's classification performance. This paper represents the first effort to demonstrate that test-set augmentation can significantly improve the classification performance of medical images. The proposed DL model was evaluated using three datasets: (a) brain magnetic resonance imaging (MRI), (b) lung computed tomography (CT) scans, and (c) cardiac MRI. The low-complexity design of the model enhances its robustness against overfitting, while it is also easily re-trainable in case out-of-distribution data is encountered, due to the reduced computational resources required by the introduced architecture.