In this work, we for the first time present a method for detecting labeling errors in image
datasets with semantic segmentation, ie, pixel-wise class labels. Annotation acquisition for …

Deep learning in the presence of noisy annotations has been studied extensively in
classification, but much less in segmentation tasks. In this work, we study the learning …

Label noise and ambiguities between similar classes are challenging problems in
developing new models and annotating new data for semantic segmentation. In this paper …

The success of modern deep learning algorithms for image segmentation heavily depends
on the availability of large datasets with clean pixel-level annotations (masks), where the …

Supervised deep learning approaches for automated diagnosis support require datasets
annotated by experts. Intra-annotator variability of a single annotator and inter-annotator …

Noisy labels can significantly affect the performance of deep neural networks (DNNs). In
medical image segmentation tasks, annotations are error-prone due to the high demand in …

Deep learning methods have achieved promising performance in many areas, but they are
still struggling with noisy-labeled images during the training process. Considering that the …

Imperfect labels limit the quality of predictions learned by deep neural networks. This is
particularly relevant in medical image segmentation, where reference annotations are …

We tackle the problem of semantic boundary prediction, which aims to identify pixels that
belong to object (class) boundaries. We notice that relevant datasets consist of a significant …

Deep learning has enabled impressive progress in the accuracy of semantic segmentation.
Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications …