We develop advanced image analysis algorithms for extracting clinically useful information from raw medical image data. This is what we call Semantic Imaging. Applications include multi-modal segmentation, anatomy recognition, object localization, image classification and learning-based image registration.
Selected publications
A geometric approach to robust medical image segmentation, Medical Image Analysis, 2024
Sheaf Theory for Robust Prostate Segmentation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023
Context Label Learning: Improving Background Class Representations in Semantic Segmentation, IEEE Transactions on Medical Imaging, 2023
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging, Medical Image Analysis, 2023
Federated learning enables big data for rare cancer boundary detection, Nature Communications, 2022
Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies, Scientific Reports, 2022
Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks, Medical Image Analysis, 2022
Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers, JAMA Network Open, 2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty, Advances in Neural Information Processing Systems (NeurIPS), 2020
Unpaired Multi-modal Segmentation via Knowledge Distillation, IEEE Transactions on Medical Imaging, 2020
Image-and-Spatial Transformer Networks for Structure-Guided Image Registration, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images, Medical Image Analysis, 2019
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation, IEEE Transactions on Medical Imaging, 2018
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth, IEEE Transactions on Medical Imaging, 2017
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation, Medical Image Analysis, 2017
