23 citations · 30 across the 5 of their papers we have counts for
7 papers
Towards IID representation learning and its application on biomedical data
Jiqing Wu, Inti Zlobec, Maxime Lafarge +2
Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In thi…
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge
Maxime W. Lafarge, Viktor H. Koelzer
Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in…
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology
Maxime W. Lafarge, Josien P. W. Pluim, Mitko Veta
Unsupervised learning enables modeling complex images without the need for annotations. The representation learned by such models can facilitate any subsequent analysis of large im…
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim +2
Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework t…
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Erik J Bekkers, Maxime W Lafarge, Mitko Veta +3
We propose a framework for rotation and translation covariant deep learning using group convolutions. The group product of the special Euclidean motion group descri…
Inferring a Third Spatial Dimension from 2D Histological Images
Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof +2
Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specim…