Matthias Minder
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View article: CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets
CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets Open
Background High-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate dete…
View article: Wasserstein-Based Graph Alignment
Wasserstein-Based Graph Alignment Open
We propose a novel method for comparing non-aligned graphs of different\nsizes, based on the Wasserstein distance between graph signal distributions\ninduced by the respective graph Laplacian matrices. Specifically, we cast a new\nformulat…
View article: Training set of microscopy images for Dietler et al. Nature Communications 2020
Training set of microscopy images for Dietler et al. Nature Communications 2020 Open
Training set of microscopy images for Dietler et al. Nature Communications 2020
View article: Training set of microscopy images for Dietler et al. Nature Communications 2020
Training set of microscopy images for Dietler et al. Nature Communications 2020 Open
Training set of microscopy images for Dietler et al. Nature Communications 2020
View article: Figlearn: Filter and Graph Learning Using Optimal Transport
Figlearn: Filter and Graph Learning Using Optimal Transport Open
In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph fil…
View article: A convolutional neural network segments yeast microscopy images with high accuracy
A convolutional neural network segments yeast microscopy images with high accuracy Open
The identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae , current segmentation methods face challenges when cells bud, c…
View article: YeaZ: A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images
YeaZ: A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images Open
The processing of microscopy images constitutes a bottleneck for large-scale experiments. A critical step is the establishment of cell borders (‘segmentation’), which is required for a range of applications such as growth or fluorescent re…
View article: Wasserstein-based Graph Alignment
Wasserstein-based Graph Alignment Open
We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, we cast a new formulation…