Work

Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks

Year: 2019

Type: conference-paper

Abstract: Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-NET) that extracts acquisition-invariant feature vectors. These can consequently be used by task-specific methods, such as voxelwise classifiers for tissue segmentation. MRAI-NET is evaluated on both simulated and real patient data. Experiments show that MRAI-NET outperforms both voxelwise classifiers trained on the source data as well as classifiers trained on the limited amount of target scanner data available.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi.2019.8759281

Authors: W.M. Kouw, M. Loog, L.W. Bartels, A.M. Mendrik

Institutions: Delft University of Technology, University Medical Center Utrecht, Netherlands eScience Center

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W2964126646

FWCI: 0.407

Cites: 9

Cited by: 8

Related to: 10