Jeroen Manders

TL;DR: The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.

TL;DR: A machine learning approach of automatic scenario identification in a dataset of public-road driving and a framework for automatic scenario extraction from real-world microscopic driving data, including measures of safety criticality are proposed.

TL;DR: In this paper, adversarial learning is used to align the source and target domains at class prediction uncertainty level by forcing the label uncertainty predictions on the target domain to be indistinguishable from those on the source domain.

TL;DR: A very simple and efficient adversarial domain adaptation method which only aligns predicted class probabilities across domains and achieves state-of-the-art results on datasets for image classification is proposed.

TL;DR: In this article, the authors proposed a two-step approach to capture scenarios from real-world data using a two step approach: the first step consists in automatically labeling the data with tags and the second step mines the scenarios, represented by a combination of tags, based on the labeled tags.