Jens Schlemper

Papers

Total Citations

H-Index

About

Jens Schlemper is a leading researcher in the field of medical imaging, with a primary focus on accelerating magnetic resonance imaging (MRI) through deep learning. His most influential work centers on the development of a novel convolutional neural network architecture, the "deep cascade of convolutional neural networks," which directly reconstructs high-quality MR images from highly undersampled data. This breakthrough, detailed in his highly cited paper "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction" (2017, over 1,500 citations), dramatically reduces scan times without compromising image fidelity, addressing a critical bottleneck in clinical MRI. Schlemper further advanced the field by introducing data-consistency layers into the network, ensuring that reconstructions remain faithful to the acquired measurements. His work has been recognized with a Best Paper Award at the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2017. Beyond reconstruction, he has also contributed to motion correction and image segmentation, with his research collectively amassing over 3,000 citations, establishing him as a key figure in the intersection of machine learning and medical imaging.