Jennifer Alvén
Jennifer Alvén is a postdoctoral researcher in the Computer Vision research group. Jennifer pursues research in the field of medical image analysis, and the focus is deep machine learning for medical image understanding and analysis. Examples of current applications are automatic echocardiography (cardiac ultrasound) analysis, and detection, segmentation and classification of plaques and stenoses in corornary CTA.
Showing 17 publications
Deep Nearest Neighbors for Anomaly Detection in Chest X-Rays
Shape-aware label fusion for multi-atlas frameworks
A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
Multiatlas Segmentation Using Robust Feature-Based Registration
Max-margin learning of deep structured models for semantic segmentation
Shape-aware multi-atlas segmentation
Überatlas: Fast and robust registration for multi-atlas segmentation
Good Features for Reliable Registration in Multi-Atlas Segmentation
Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
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Showing 1 research projects
Deep Learning for Extracting Tree Structures in Medical Images