… uncertainty estimation has been applied to the task of medical image classification. We also
… which metrics are used to describe the effectiveness of the applied uncertainty estimation …

… In this work, we explore the use of uncertainty estimation techniques and metrics for deep …
classification on data from ISIC Challenges 2018 and 2019. Our results show that uncertainty …

… Accordingly, we combined Transfer Learning methods and Uncertainty Quantification …
on medical classification tasks. This research explores uncertainty on classification tasks for …

… of uncertainty. For robotics applications, fast (close to real-time) estimates of uncertainty are
… still produces high quality uncertainty estimates in image classification tasks. This allows for …

… for quantifying uncertainty in image classification models, … ), and not a pure uncertainty
estimation technique. Also, the … comparing across different uncertainty estimators, besides …

… trained, to evaluate the uncertainty estimations. We found that … Our results show that the
uncertainty estimation on its own … Overall, the addition of an uncertainty estimate proves useful …

… a novel method that combines uncertainty, diversity, and … We use sparse linear combination
to represent the uncertainty of … the uncertainty influence, Ii, j , as a weighted uncertainty …

… classification uncertainty based on the local probabilities of class membership. We apply this
method to SAR image … the dataset decreases the local uncertainty estimates. Polarimetric L- …

… We then propose an uncertainty estimation-based framework to handle these two label
noise amid the medical image classification task. We design a dual-uncertainty estimation …

… noise, which affects radar image interpretation. Recently, … , in general, lack uncertainty
quantification and can be easily … SAR image classification while quantifying the uncertainty or …