We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
Publications - page 6
Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation
Visual saliency-based active learning for prostate magnetic resonance imaging segmentation
TI-Pooling: Transformation-invariant pooling for feature learning in convolutional neural networks
Oxygen supply maps for hypoxic microenvironment visualization in prostate cancer
Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
Information-theoretic analysis of MaxCut algorithms
NP-hard combinatorial optimization algorithms are often characterized by their approximation ratios. In real world applications, the resilience of algorithms to input fluctuations and to modelling errors pose important robustness requirements. This work suggests a provable algorithmic regularization and validation strategy based on posterior agreement. The strategy regularizes algorithms and ranks them according to the informativeness of their output given noisy input. To illustrate this strategy, we develop methods to evaluate the posterior distribution of the Goemans-Williamson's MaxCut algorithm using semidefinite...
Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity
Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based...