|The liver tumor segmentation benchmark (LITS)|
P Bilic, PF Christ, E Vorontsov, G Chlebus, H Chen, Q Dou, CW Fu, X Han, ...
arXiv preprint arXiv:1901.04056, 2019
|Secure, privacy-preserving and federated machine learning in medical imaging|
GA Kaissis, MR Makowski, D Rückert, RF Braren
Nature Machine Intelligence 2 (6), 305-311, 2020
|Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks|
PF Christ, F Ettlinger, F Grün, MEA Elshaera, J Lipkova, S Schlecht, ...
arXiv preprint arXiv:1702.05970, 2017
|End-to-end privacy preserving deep learning on multi-institutional medical imaging|
G Kaissis, A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, ...
Nature Machine Intelligence 3 (6), 473-484, 2021
|Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study|
Q Dou, TY So, M Jiang, Q Liu, V Vardhanabhuti, G Kaissis, Z Li, W Si, ...
NPJ digital medicine 4 (1), 60, 2021
|Pysyft: A library for easy federated learning|
A Ziller, A Trask, A Lopardo, B Szymkow, B Wagner, E Bluemke, ...
Federated Learning Systems: Towards Next-Generation AI, 111-139, 2021
|Intensive care risk estimation in COVID-19 pneumonia based on clinical and imaging parameters: experiences from the Munich cohort|
E Burian, F Jungmann, GA Kaissis, FK Lohöfer, CD Spinner, T Lahmer, ...
Journal of clinical medicine 9 (5), 1514, 2020
|Medical imaging deep learning with differential privacy|
A Ziller, D Usynin, R Braren, M Makowski, D Rueckert, G Kaissis
Scientific Reports 11 (1), 13524, 2021
|A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging|
G Kaissis, S Ziegelmayer, F Lohöfer, H Algül, M Eiber, W Weichert, ...
European radiology experimental 3 (1), 1-9, 2019
|A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy|
G Kaissis, S Ziegelmayer, F Lohöfer, K Steiger, H Algül, A Muckenhuber, ...
PloS one 14 (10), e0218642, 2019
|Image-based molecular phenotyping of pancreatic ductal adenocarcinoma|
GA Kaissis, S Ziegelmayer, FK Lohöfer, FN Harder, F Jungmann, D Sasse, ...
Journal of Clinical Medicine 9 (3), 724, 2020
|SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks|
PF Christ, F Ettlinger, G Kaissis, S Schlecht, F Ahmaddy, F Grün, ...
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017
|Ratchet: Medical transformer for chest x-ray diagnosis and reporting|
B Hou, G Kaissis, RM Summers, B Kainz
Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021
|Joint imaging platform for federated clinical data analytics|
J Scherer, M Nolden, J Kleesiek, J Metzger, K Kades, V Schneider, ...
JCO clinical cancer informatics 4, 1027-1038, 2020
|Implementing cell-free DNA of pancreatic cancer patient–derived organoids for personalized oncology|
Z Dantes, HY Yen, N Pfarr, C Winter, K Steiger, A Muckenhuber, A Hennig, ...
JCI insight 5 (15), 2020
|Joint learning of localized representations from medical images and reports|
P Müller, G Kaissis, C Zou, D Rueckert
European Conference on Computer Vision, 685-701, 2022
|A primer on machine learning|
J Kleesiek, JM Murray, C Strack, G Kaissis, R Braren
Der Radiologe 60, 24-31, 2020
|Magnetic resonance cholangiopancreatography at 3 Tesla: image quality comparison between 3D compressed sensing and 2D single-shot acquisitions|
FK Lohöfer, GA Kaissis, M Rasper, C Katemann, A Hock, JM Peeters, ...
European Journal of Radiology 115, 53-58, 2019
|Accuracy of calcium scoring calculated from contrast-enhanced coronary computed tomography angiography using a dual-layer spectral CT: a comparison of calcium scoring from real …|
J Nadjiri, G Kaissis, F Meurer, F Weis, KL Laugwitz, AS Straeter, ...
PLoS One 13 (12), e0208588, 2018
|Adversarial interference and its mitigations in privacy-preserving collaborative machine learning|
D Usynin, A Ziller, M Makowski, R Braren, D Rueckert, B Glocker, ...
Nature Machine Intelligence 3 (9), 749-758, 2021