Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing th...
Mert R. Sabuncu, B. T. Thomas Yeo, Koen Van Leem...
This paper describes the participation of Idiap-MULTI to the Robot Vision Task at imageCLEF 2010. Our approach was based on a discriminative classification algorithm using multiple...
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Abstract. Volume representations of blood vessels acquired by 3D rotational angiography are very suitable for diagnosing an aneurysm. We presented a fully-automatic aneurysm labell...
Jan Bruijns, Frans J. Peters, Robert-Paul Berretty...
The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled tra...