Abstract Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper …
J Hasenauer, N Jagiella, S Hross… - Journal of Coupled …, 2015 - ingentaconnect.com
Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which …
V Wegmayr, JM Buhmann - International Journal of Computer Vision, 2021 - Springer
White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain …
Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling …
We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with …
MH Chehreghani… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and …
This paper introduces a method to predict and analyse students' mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We …
NS Gorbach, AA Bian, B Fischer, S Bauer… - … , GCPR 2017, Basel …, 2017 - Springer
Gaussian processes are powerful tools since they can model non-linear dependencies between inputs, while remaining analytically tractable. A Gaussian process is characterized …
Several clustering methods (eg, Normalized Cut and Ratio Cut) divide the Min Cut cost function by a cluster dependent factor (eg, the size or the degree of the clusters), in order to …