Jennifer Alvén
Jennifer Alvén is an Assistant Professor in the Computer Vision and Medical Image Analysis group at the Department of Electrical Engineering, Chalmers University of Technology. Her research focuses on deep learning for medical image analysis. In collaboration with clinical partners, she explores AI methods automatic echocardiography analysis, segmentation of atherosclerotic plaques in coronary CT angiography, weakly supervised anomaly detection in PET lymphoma, emphysema analysis in chest CT and for detecting complications after endovascular aortic repair (EVAR) in abdominal CT. Jennifer is particularly interested in generative models for medical image-to-image translation, and in building foundation models for medical imaging that can generalize across modalities and tasks. News August 2025: "Patch-level attribution of multimodal fracture risk prediction" (Wåhlstrand et al.) accepted to MLMI 2025 June 2025: "Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes" (Naeem et al.) accepted to MICCAI 2025 June 2025: "Separable tissue representations for attributable risk prediction" (Wåhlstrand et al.) accepted to MICCAI 2025
Showing 20 publications
NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations
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