116 citations · 240 across the 35 of their papers we have counts for
7 papers · 1 filter
Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images
Suzanne C. Wetstein, Nikolas Stathonikos, Josien P. W. Pluim +5
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerab…
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…
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil +4
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with…
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim +1
Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnosti…
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks
Friso G. Heslinga, Mark Alberti, Josien P. W. Pluim +2
Purpose: We developed a method to automatically locate and quantify graft detachment after Descemet's Membrane Endothelial Keratoplasty (DMEK) in Anterior Segment Optical Coherence…
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…