Schlemper, Jo

Attention U-Net: Learning Where to Look for the Pancreas

Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 5,458

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 2,014

A deep cascade of convolutional neural networks for dynamic MR image reconstruction

Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N., & Rueckert, D. (2018) · IEEE Transactions on Medical Imaging

Advanced MRI Techniques and ApplicationsSparse and Compressive Sensing Techniques

Cited by 1,338

Automatic 3d bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 227

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

Meng, Q., Housden, J., Matthew, J., Rueckert, D., Schnabel, J. A., Kainz, B., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., et al. (2019) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 52