23 citations · 62 across the 9 of their papers we have counts for
10 papers · 1 filter
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Marc Aubreville, Christof Bertram, Mitko Veta +6
Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, i…
Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels
Christof A. Bertram, Mitko Veta, Christian Marzahl +4
Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mito…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
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…
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…
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…