Modeling and Understanding Uncertainty in Medical Image Classification

Aobo Chen, Yangyi Li, Wei Qian, Kathy Morse, Chenglin Miao, Mengdi Huai

Abstract: Medical image classification is an important task in many different medical applications. The past years have witnessed the success of Deep Neural Networks (DNNs) in medical image classification. However, traditional softmax outputs produced by DNNs fail to estimate uncertainty in medical image predictions. Contrasting with conventional uncertainty estimation approaches, conformal prediction (CP) stands out as a model-agnostic and distribution-free methodology that constructs statistically rigorous uncertainty sets for model predictions. However, existing exact full conformal methods involve retraining the underlying DNN model for each test instance with each possible label, demanding substantial computational resources. Additionally, existing works fail to uncover the root causes of medical prediction uncertainty, making it difficult for doctors to interpret the estimated uncertainties associated with medical diagnoses. To address these challenges, in this paper, we first propose an efficient approximate full CP method, which involves tracking the gradient updates contributed by these samples during training. Subsequently, we design an interpretation method that uses these updates to identify the top-k most influential training samples that significantly impact models’ uncertainties. Extensive experiments on real-world medical image datasets are conducted to verify the effectiveness of the proposed methods.