Uncertainty Estimation for Tumor Prediction with Unlabeled Data

Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper, we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology, we propose to adopt novel learning method that can fully exploit unlabeled data. The proposed method achieves superior performance compared with different baselines including the celebrated Monte-Carlo Dropout. Closeup inspection of uncertain regions reveal insight into the model and improves the trustworthiness of the models.