Shadi Albarqouni
Shadi Albarqouni
Professor of Computational Medical Imaging Research @Uni. Bonn | AI Group Leader @HelmholtzMunich
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Cited by
Cited by
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ...
Jama 318 (22), 2199-2210, 2017
The future of digital health with federated learning
N Rieke, J Hancox, W Li, F Milletari, H Roth, S Albarqouni, S Bakas, ...
npj Digital Medicine 3 (119), 2020
Structure-preserving color normalization and sparse stain separation for histological images
A Vahadane, T Peng, A Sethi, S Albarqouni, L Wang, M Baust, K Steiger, ...
IEEE transactions on medical imaging 35 (8), 1962-1971, 2016
Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images
S Albarqouni, C Baur, F Achilles, V Belagiannis, S Demirci, N Navab
IEEE transactions on medical imaging 35 (5), 1313-1321, 2016
Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
C Baur, B Wiestler, S Albarqouni, N Navab
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019
GANs for medical image analysis
S Kazeminia, C Baur, A Kuijper, B van Ginneken, N Navab, S Albarqouni, ...
Artificial intelligence in medicine 109, 101938, 2020
Staingan: Stain style transfer for digital histological images
MT Shaban, C Baur, N Navab, S Albarqouni
2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019 …, 2019
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
C Baur, S Denner, B Wiestler, N Navab, S Albarqouni
Medical Image Analysis 101952, 2021
Generating highly realistic images of skin lesions with GANs
C Baur, S Albarqouni, N Navab
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy …, 2018
InceptionGCN: receptive field aware graph convolutional network for disease prediction
A Kazi, S Shekarforoush, S Arvind Krishna, H Burwinkel, G Vivar, ...
Information Processing in Medical Imaging: 26th International Conference …, 2019
Semi-supervised deep learning for fully convolutional networks
C Baur, S Albarqouni, N Navab
Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017
An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
S Ali, F Zhou, B Braden, A Bailey, S Yang, G Cheng, P Zhang, X Li, ...
Scientific reports 10 (1), 2748, 2020
Capsule networks against medical imaging data challenges
A Jiménez-Sánchez, S Albarqouni, D Mateus
Intravascular Imaging and Computer Assisted Stenting and Large-Scale …, 2018
Deep learning and data labeling for medical applications
G Carneiro, D Mateus, L Peter, A Bradley, JMRS Tavares, V Belagiannis, ...
Inverse distance aggregation for federated learning with non-iid data
Y Yeganeh, A Farshad, N Navab, S Albarqouni
Domain Adaptation and Representation Transfer, and Distributed and …, 2020
Fairness by Learning Orthogonal Disentangled Representations
MH Sarhan, N Navab, A Eslami, S Albarqouni
16th European Conference on Computer Vision (ECCV), 2020
Image-to-images translation for multi-task organ segmentation and bone suppression in chest x-ray radiography
M Eslami, S Tabarestani, S Albarqouni, E Adeli, N Navab, M Adjouadi
IEEE transactions on medical imaging 39 (7), 2553-2565, 2020
Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings
J Ogier du Terrail, SS Ayed, E Cyffers, F Grimberg, C He, R Loeb, ...
Advances in Neural Information Processing Systems 35, 5315-5334, 2022
The federated tumor segmentation (fets) challenge
S Pati, U Baid, M Zenk, B Edwards, M Sheller, GA Reina, P Foley, ...
arXiv preprint arXiv:2105.05874, 2021
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
A Lahiani, N Navab, S Albarqouni, E Klaiman
Medical Image Computing and Computer Assisted Intervention, 568-576, 2019
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