Robust Multi-Modal MR Image Synthesis

Abstract:

We present a multi-input encoder-decoder neural network model able to perform MR image synthesis from any subset of its inputs, outperforming prior methods in both single and multi-input settings. This is achieved by encouraging the network to learn a modality invariant latent embedding during training. We demonstrate that a spatial transformer module can be included in our model to automatically correct misalignment in the input data. Thus, our model is robust both to missing and misaligned data at test time. Finally, we show that the model's modular nature allows transfer learning to different datasets.

Biography:

Agisilaos Chartsias received his diploma degree in Electronic Engineering and Computer Science from the Technical University of Crete, Greece, followed by a MSc in Artificial Intelligence from the University of Edinburgh. He is currently a PhD student in the University of Edinburgh under the supervision of Dr. Sotirios Tsaftaris working on deep learning for medical image analysis.