Information Science and Engineering Lab

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.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Follow Us

Information Science and Engineering Lab

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.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Follow Us

Others - page 12

A model development pipeline for Crohn's disease severity assessment from magnetic resonance images

Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A.W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor, Joachim M. Buhmann,

Abdominal Imaging. Computation and Clinical Applications : 5th International Workshop, Held in Conjunction with MICCAI 2013, 8198

Research Collection

A Supervised Learning Approach for Crohn's Disease Detection Using Higher-Order Image Statistics and a Novel Shape Asymmetry Measure

Dwarikanath Mahapatra, Peter J. Schueffler, Jeroen A.W. Tielbeek, Joachim M. Buhmann, Franciscus M. Vos,

Journal of digital imaging, 26

DOI: 10.3929/ethz-b-000072543      Research Collection

Weakly Supervised Structured Output Learning for Semantic Segmentation

Alexander Vezhnevets, Vittorio Ferrari, Joachim M. Buhmann,

IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012),

Research Collection

Unsupervised modeling of cell morphology dynamics for time-lapse microscopy

Qing Zhong, Alberto Giovanni Busetto, Juan P. Fededa, Joachim M. Buhmann, Daniel W. Gerlich,

Nature Methods, 9

Research Collection

The Information Content in Sorting Algorithms

Ludwig M. Busse, Morteza Haghir Chehreghani, Joachim M. Buhmann,

IEEE International Symposium on Information Theory (ISIT) 2012,

Research Collection

Speech Enhancement Using Generative Dictionary Learning

C.D. Sigg, T. Dikk, Joachim M. Buhmann,

IEEE Transactions on Audio, Speech, and Language Processing, 20

Research Collection

Probabilistic image registration and anomaly detection by nonlinear warping

Verena Kaynig, Bernd Fischer, Joachim M. Buhmann,

Technical report / Computer Science Department, ETH Zürich, 585

DOI: 10.3929/ethz-a-006820379      Research Collection

Automatic, defect tolerant registration of transmission electron microscopy (TEM) images poses an important and challenging problem for biomedical image analysis, e.g. in computational neuroanatomy. In this paper we demonstrate a fully automatic stitching and distortion correction method for TEM images and propose a probabilistic approach for image registration that implicitly detects image defects due to sample preparation and image acquisition. The approach uses a polynomial kernel expansion to estimate a non-linear image transformation based on intensities and spatial features. Corresponding...

Multi-assignment clustering for Boolean data

Mario Frank, Andreas P. Streich, David Basin, Joachim M. Buhmann,

Journal of Machine Learning Research, 13

Research Collection

Learning Dictionaries With Bounded Self-Coherence

C.D. Sigg, T. Dikk, Joachim M. Buhmann,

IEEE Signal Processing Letters, 19

Research Collection