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Maxime Lafarge
Maxime Lafarge
Postdoctoral Researcher, University Hospital Zürich
Verified email at usz.ch
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Cited by
Cited by
Year
Adversarial training and dilated convolutions for brain MRI segmentation
P Moeskops, M Veta, MW Lafarge, KAJ Eppenhof, JPW Pluim
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017
1542017
Roto-translation covariant convolutional networks for medical image analysis
EJ Bekkers, MW Lafarge, M Veta, KAJ Eppenhof, JPW Pluim, R Duits
Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018
1272018
Domain-adversarial neural networks to address the appearance variability of histopathology images
MW Lafarge, JPW Pluim, KAJ Eppenhof, P Moeskops, M Veta
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017
1132017
Deformable image registration using convolutional neural networks
KAJ Eppenhof, MW Lafarge, P Moeskops, M Veta, JPW Pluim
Medical Imaging 2018: Image Processing 10574, 192-197, 2018
1052018
Roto-translation equivariant convolutional networks: Application to histopathology image analysis
MW Lafarge, EJ Bekkers, JPW Pluim, R Duits, M Veta
Medical Image Analysis 68, 101849, 2021
422021
Progressively trained convolutional neural networks for deformable image registration
KAJ Eppenhof, MW Lafarge, M Veta, JPW Pluim
IEEE transactions on medical imaging 39 (5), 1594-1604, 2019
392019
Learning domain-invariant representations of histological images
MW Lafarge, JPW Pluim, KAJ Eppenhof, M Veta
Frontiers in medicine 6, 162, 2019
332019
Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning
MW Lafarge, JC Caicedo, AE Carpenter, JPW Pluim, S Singh, M Veta
International Conference on Medical Imaging with Deep Learning (MIDL) 102 …, 2018
242018
Multimodal angiographic assessment of cerebral arteriovenous malformations: a pilot study
R Blanc, A Seiler, T Robert, H Baharvahdat, M Lafarge, J Savatovsky, ...
Journal of neurointerventional surgery 7 (11), 841-847, 2015
172015
Mitosis domain generalization in histopathology images—The MIDOG challenge
M Aubreville, N Stathonikos, CA Bertram, R Klopfleisch, N Ter Hoeve, ...
Medical Image Analysis 84, 102699, 2023
122023
Progressively growing convolutional networks for end-to-end deformable image registration
KAJ Eppenhof, MW Lafarge, JPW Pluim
Medical Imaging 2019: Image Processing 10949, 338-344, 2019
112019
Orientation-disentangled unsupervised representation learning for computational pathology
MW Lafarge, JPW Pluim, M Veta
arXiv preprint arXiv:2008.11673, 2020
42020
Rotation invariance and extensive data augmentation: A strategy for the mitosis domain generalization (MIDOG) challenge
MW Lafarge, VH Koelzer
Biomedical Image Registration, Domain Generalisation and Out-of-Distribution …, 2022
22022
Towards IID representation learning and its application on biomedical data
J Wu, I Zlobec, M Lafarge, Y He, VH Koelzer
arXiv preprint arXiv:2203.00332, 2022
22022
Towards computationally efficient prediction of molecular signatures from routine histology images
MW Lafarge, VH Koelzer
The Lancet Digital Health 3 (12), e752-e753, 2021
22021
Inferring a third spatial dimension from 2D histological images
MW Lafarge, JPW Pluim, KAJ Eppenhof, P Moeskops, M Veta
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018
22018
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset
MW Lafarge, VH Koelzer
arXiv preprint arXiv:2301.01079, 2023
2023
Enhancing local context of histology features in vision transformers
R Wood, K Sirinukunwattana, E Domingo, A Sauer, MW Lafarge, ...
Artificial Intelligence over Infrared Images for Medical Applications and …, 2022
2022
Learning Invariant Representations of Images for Computational Pathology
MW Lafarge
Technische Universiteit Eindhoven, 2021
2021
Training deep convolutional neural networks for deformable image registration of pulmonary CT images
K Eppenhof, M Lafarge, J Pluim
Workshop on Biomedical Image Registration, 2018
2018
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