31 citations · 103 across the 26 of their papers we have counts for
5 papers · 1 filter
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
Crowd disagreement about medical images is informative
Veronika Cheplygina, Josien P. W. Pluim
Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotato…
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised…
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…