Lehrstuhl für KI in der Medizin

Hier finden Sie eine Aufstellung unserer aktuellen Publikationen.

Key journal publications since 2020

  • D. Rueckert and J. A. Schnabel. Model-Based and Data-Driven Strategies in Medical Image Computing. Proceedings of the IEEE 108(1): 110-124, 2020.
  • G. A. Kaissis, M. R. Makowski, D. Rueckert and R. F. Braren. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence 2: 305–311, 2020.
  • C. Chen, C. Qin, H. Qiu, G. Tarroni, J. Duan, W. Bai and D. Rueckert. Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in Cardiovascular Medicine, 7, 2020.
  • W. Bai, H. Suzuki, J. Huang, C. Francis, S. Wang, G. Tarroni, F. Guitton, N. Aung, K. Fung, S. E. Petersen, S. K. Piechnik, S. Neubauer, E. Evangelou, A. Dehghan, D. P. O’Regan, M. R. Wilkins, Y. Guo, P. M. Matthews and D. Rueckert. A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine 26:1654–1662, 2020.
  • H. V. Meyer, T. J. W. Dawes, M. Serrani, W. Bai, P. Tokarczuk, J. Cai, A. de Marvao, A. Henry, R. Thomas Lumbers, J. Gierten, T. Thumberger, J. Wittbrodt, J. S. Ware, D. Rueckert, Paul M. Matthews, S. K. Prasad, M. L. Costantino, S. A. Cook, E. Birney and D. P. O'Regan. Genetic and functional insights into the fractal structure of the heart. Nature 584: 589–594, 2020.
  • S. P. Fitzgibbon, S. J. Harrison, M. Jenkinson, L. Baxter, E. C. Robinson, M. Bastiani, J. Bozek, V. Karolis, L. Cordero Grande, A. N. Price, E. Hughes, A. Makropoulos, J. Passerat-Palmbach, A. Schuh, J. Gao, S. R. Farahibozorg, J. O'Muircheartaigh, J. Ciarrusta, C. O'Keeffe, J. Brandon, T. Arichi, D. Rueckert, J. V. Hajnal, A. D. Edwards, S. M. Smith, E. Duff and J. Andersson. The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. NeuroImage, 223, 2020.
  • S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert and R. M. Summers. A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies with Progress Highlights, and Future Promises. Proceedings of the IEEE, 2021.
  • G. Kaissis, A. Ziller, J. Passerat-Palmbach, T. Ryffel, D. Usynin, A. Trask, I. D. L. Costa Junior, J. Mancuso, F. Jungmann, M.-M. Steinborn, A. Saleh, M. Makowski, D. Rueckert and R. Braren, End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3:473–484, 2021.
  • Q. Dou, T. Y. So, M. Jiang, Q. Liu, V. Vardhanabhuti, G. Kaissis, Z. Li, W. Si, H. H. C. Lee, K. Yu, Z. Feng, L. Dong, E. Burian, F. Jungmann, R. Braren, M. Makowski, B. Kainz, D. Rueckert, B. Glocker, S. C. H. Yu and P. A. Heng. Federated deep learning for detecting COVID-19 lung abnormalities in CT: A privacy-preserving multinational validation study. npj Digital Medicine 60(4), 2021.
  • D. Usynin, A. Ziller, M. Makowski, R. Braren, D. Rueckert, B. Glocker, G. Kaissis and J. Passerat-Palmbach. Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nature Machine Intelligence (3), 749–758 2021.
  • A. Ziller, D. Usynin, R. Braren, M. Makowski, D. Rueckert and G. Kaissis. Medical imaging deep learning with differential privacy. Scientific Reports, 11:13524, 2021.
  • Y. Chen, C.-B. Schönlieb, P. Liò, T, Leiner, P. L. Dragotti, G. Wang, D. Rueckert, D. N. Firmin and G. Yang. AI-Based Reconstruction for Fast MRI - A Systematic Review and Meta-Analysis. Proc. IEEE 110(2): 224-245, 2022.

Key conference publications since 2020

  • C. Ouyang, C. Biffi, C. Chen, T. Kart, H. Qiu and D. Rueckert. Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation. ECCV, 762-780, 2020.
  • S. Wang, G. Tarroni, C. Qin, Y. Mo, C. Dai, C. Chen, B. Glocker, Y. Guo, D. Rueckert and W. Bai: Deep Generative Model-Based Quality Control for Cardiac MRI Segmentation. MICCAI, 88-97, 2020.
  • E. Puyol-Antón, C. Chen, J. R. Clough, B. Ruijsink, B. S. Sidhu, J. Gould, B. Porter, M. Elliott, V. Mehta, D. Rueckert, C. A. Rinaldi and A. P. King: Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction. MICCAI, 284-293, 2020.
  • C. Qin, S. Wang, C. Chen, H. Qiu, W. Bai and D. Rueckert: Biomechanics-Informed Neural Networks for Myocardial Motion Tracking in MRI. MICCAI, 296-306, 2020.
  • A. Gasimova, G. Seegoolam, L. Chen, P. Bentley and D. Rueckert: Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation. MICCAI, 333-342, 2020.
  • C. Chen, C. Qin, H. Qiu, C. Ouyang, S. Wang, L. Chen, G. Tarroni, W. Bai and D. Rueckert: Realistic Adversarial Data Augmentation for MR Image Segmentation. MICCAI, 667-677, 2020.
  • T. Liu, Q. Meng, A. Vlontzos, J. Tan, D. Rueckert and B. Kainz: Ultrasound Video Summarization Using Deep Reinforcement Learning. MICCAI, 483-492, 2020.
  • R. Robinson, Q. Dou, D. Coelho de Castro, K. Kamnitsas, M. de Groot, R. M. Summers, D. Rueckert and B. Glocker: Image-Level Harmonization of Multi-site Data Using Image-and-Spatial Transformer Networks. MICCAI, 710-719, 2020.
  • P. Lu, W. Bai, D. Rueckert, J. A. Noble: Multiscale Graph Convolutional Networks for Cardiac Motion Analysis. FIMH: 264-272, 2021.
  • P. Lu, W. Bai, D. Rueckert, J. A. Noble: Dynamic Spatio-Temporal Graph Convolutional Networks For Cardiac Motion Analysis. ISBI: 122-125, 2021.
  • O. N. Hassan, M. J. Menten, H. Bogunovic, U. Schmidt-Erfurth, A. Lotery and D. Rueckert: Deep Learning Prediction Of Age And Sex From Optical Coherence Tomography. ISBI: 238-242, 2021.
  • J. Tan, B. Hou, T. Day, J. M. Simpson, D. Rueckert and B. Kainz: Detecting Outliers with Poisson Image Interpolation. MICCAI (5): 581-591, 2021.
  • S. Wang, C. Qin, N. Savioli, C. Chen, D. P. O'Regan, S. A. Cook, Y. Guo, D. Rueckert and W. Bai: Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation. MICCAI (3): 14-24, 2021.
  • C. Chen, K. Hammernik, C. Ouyang, C. Qin, W. Bai and D. Rueckert: Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation. MICCAI (3): 149-159, 2021.
  • S. Budd, M. Sinclair, T. Day, A. Vlontzos, J. Tan, T. Liu, J. Matthew, E. Skelton, J. M. Simpson, R. Razavi, B. Glocker, D. Rueckert, E. C. Robinson and B. Kainz: Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. MICCAI (7): 207-217, 2021.
  • H. Qiu, C. Qin, A. Schuh, K. Hammernik and D. Rueckert: Learning Diffeomorphic and Modality-invariant Registration using B-splines. MIDL: 645-664, 2021.