Uncertainty-Aware Ensemble Learning Models for Out-of-Distribution Medical Imaging Analysis
J. Ben Tamo, Micky C. Nnamdi, Lea Lesbats, Wenqi Shi, Yishan Zhong, May Dongmei Wang · 2023
Advanced deep-learning techniques have been employed to develop clinical decision support systems for diagnosis and prognosis using medical images. However, the presence of out-of-distribution (OOD) samples, which deviate from the training data distribution, poses a significant challenge. Accurate quantification of the predictive uncertainty is crucial for ensuring reliable and dependable implementation in medical settings as a clinical decision support system. In this work, we propose an ensemble model to derive predictive uncertainty estimates for uncertainty quantification on OOD medical imaging. Specifically, the models are initialized with ImageNet pre-trained weights and fine-tuned on chest Computed Tomography (CT). Moreover, we utilize Grad-CAM to visualize and interpret the areas of the image that contribute most to the model’s predictions and uncertainty estimates. This visualization technique enhances the in-terpretability of our ensemble model and supports more informed clinical decision-making. Through extensive experiments on three Chest CT datasets, we have demonstrated the effectiveness of our approach in estimating uncertainty under domain shifting. Our results provide valuable insights into the reliability and specificity of deep ensemble uncertainty predictions in medical image analysis. Our Uncertainty-Aware Ensemble (UAE) approach can enable reliable and transparent predictions for safety-critical medical applications.