Office: Na 26-16
I work on transfer learning and feature learning, and their applications in model-based medical image analysis. I am interested in the theoretical and practical aspects of machine learning and computer vision.
G. van Tulder and M. de Bruijne, Learning Cross-Modality Representations from Multi-Modal Images, IEEE Transactions on Medical Imaging, 2018
G. van Tulder and M. de Bruijne, Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines, IEEE Transactions on Medical Imaging, 2016
G. van Tulder and M. de Bruijne, Representation Learning for Cross-Modality Classification, MICCAI 2016 Workshop on Medical Computer Vision, LNCS 10081, 2017
G. van Tulder and M. de Bruijne, Why Does Synthesized Data Improve Multi-Sequence Classification?, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, LNCS 9349, 2015
G. van Tulder and M. de Bruijne, Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine, MICCAI 2014 Workshop on Medical Computer Vision: Algorithms for Big Data, LNCS 8848, 2014