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

Journal Article

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

Fabian Laumer, Davide Di Vece, Victoria L. Cammann, Michael Würdinger, Vanya Petkova, Maximilian Schönberger, Alexander Schönberger, Julien C. Mercier, David Niederseer, Burkhardt Seifert, Moritz Schwyzer, Rebekka Burkholz, Luca Corinzia, Anton S. Becker, Frank Scherff, Sofie Brouwers, Aju P. Pazhenkottil, Svetlana Dougoud, Michael Messerli, Felix C. Tanner, Thomas Fischer, Victoria Delgado, P. Christian Schulze, Christian Hauck, Lars S. Maier, Ha Nguyen, Sven Y. Surikow, John Horowitz, Kan Liu, Rodolfo Citro, Jeroen Bax, Frank Ruschitzka, Jelena-Rima Ghadri, Joachim M. Buhmann, Christian Templin,

JAMA Cardiology, 7

Research Collection

Importance Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry...

Self-supervised representation learning for surgical activity recognition

Daniel Paysan, Luis Haug, Michael Bajka, Markus Oelhafen, Joachim M. Buhmann,

International Journal of Computer Assisted Radiology and Surgery, 16

DOI: 10.3929/ethz-b-000507286      Research Collection

Purpose: Virtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential, they must be able to automatically recognize activities performed by users and assess those. Since annotations of trajectories by human experts are expensive, there is a need for methods that can learn to recognize surgical activities in a data-efficient way. Methods: We use self-supervised training of deep encoder-decoder architectures to learn representations of surgical trajectories from video data....

Rapid and reversible control of human metabolism by individual sleep states

Nora Nowak, Thomas Gaisl, Djordje Miladinovic, Ricards Marcinkevics, Martin Osswald, Stefan Bauer, Joachim Buhmann, Renato Zenobi, Pablo Sinues, Steven A. Brown, Malcolm Kohler,

Cell Reports, 37

DOI: 10.3929/ethz-b-000513757      Research Collection

Sleep is crucial to restore body functions and metabolism across nearly all tissues and cells, and sleep restriction is linked to various metabolic dysfunctions in humans. Using exhaled breath analysis by secondary electrospray ionization high-resolution mass spectrometry, we measured the human exhaled metabolome at 10-s resolution across a night of sleep in combination with conventional polysomnography. Our subsequent analysis of almost 2,000 metabolite features demonstrates rapid, reversible control of major metabolic pathways by the individual vigilance states. Within this framework,...

Prognostic value of inflammatory biomarkers and GRACE score for cardiac death and acute kidney injury after acute coronary syndromes

Valentina A. Rossi, Andrea Denegri, Alessandro Candreva, Roland Klingenberg, Slayman Obeid, Lorenz Raeber, Baris Gencer, François Mach, David Nanchen, Nicolas Rodondi, Dik Heg, Stephan Windecker, Joachim Buhmann, Frank Ruschitzka, Thomas F. Lüscher, Christian M. Matter,

European Heart Journal – Acute Cardiovascular Care, 10

Research Collection

Aims  The aim of this study was to analyse the role of inflammation and established clinical scores in predicting acute kidney injury (AKI) after acute coronary syndromes (ACS). Methods and results  In a prospective multicentre cohort including 2034 patients with ACS undergoing percutaneous coronary intervention, high-sensitivity C-reactive protein (hsCRP), neutrophil count, neutrophil-to-lymphocyte ratio (NL-ratio), and creatinine were measured at the index procedure. AKI (n = 39, defined according to RIFLE criteria) and major cardiovascular and cerebrovascular events were adjudicated after 1 year. Associations...

Improving 1-year mortality prediction in ACS patients using machine learning

Sebastian Weichwald, Alessandro Candreva, Rebekka Burkholz, Roland Klingenberg, Lorenz Raber, Dik Heg, Robert Manka, Baris Gencer, François Mach, David Nanchen, Nicolas Rodondi, Stephan Windecker, Reijo Laaksonen, Stanley L. Hazen, Arnold von Eckardstein, Frank Ruschitzka, Thomas F. Lüscher, Joachim M. Buhmann, Christian M. Matter,

European Heart Journal – Acute Cardiovascular Care, 10

Research Collection

Background The Global Registry of Acute Coronary Events (GRACE) score is an established clinical risk stratification tool for patients with acute coronary syndromes (ACS). We developed and internally validated a model for 1-year all-cause mortality prediction in ACS patients. Methods Between 2009 and 2012, 2’168 ACS patients were enrolled into the Swiss SPUM-ACS Cohort. Biomarkers were determined in 1’892 patients and follow-up was achieved in 95.8% of patients. 1-year all-cause mortality was 4.3% (n = 80). In our analysis we consider all...

Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography

Viktor Wegmayr, Joachim M. Buhmann,

International Journal of Computer Vision, 129

DOI: 10.3929/ethz-b-000451919      Research Collection

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 connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is...

Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography

Luca Corinzia, Fabian Laumer, Alessandro Candreva, Maurizio Taramasso, Francesco Maisano, Joachim M. Buhmann,

Artificial intelligence in medicine, 110

Research Collection

The segmentation of the mitral valve annulus and leaflets specifies a crucial first step to establish a machine learning pipeline that can support physicians in performing multiple tasks, e.g. diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. Current methods for mitral valve segmentation on 2D echocardiography videos require extensive interaction with annotators and perform poorly on low-quality and noisy videos. We propose an automated and unsupervised method for the mitral valve segmentation based on a low dimensional embedding...

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Đorđe Miladinović, Christine Muheim, Stefan Bauer, Andrea Spinnler, Daniela Noain, Mojtaba Bandarabadi, Benjamin Gallusser, Gabriel Krummenacher, Christian Baumann, Antoine Adamantidis, Steven A. Brown, Joachim M. Buhmann,

PLoS Computational Biology, 15

DOI: 10.3929/ethz-b-000342836      Research Collection

Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalization capabilities are often insufficient, especially across animals from...

Wheel Defect Detection With Machine Learning

Gabriel Krummenacher, Cheng S. Ong, Stefan Koller, Seijin Kobayashi, Joachim M. Buhmann,

IEEE Transactions on Intelligent Transportation Systems, 19

Research Collection

Validity of GRE General Test scores and TOEFL scores for graduate admission to a technical university in Western Europe

Judith Zimmermann, Alina A. von Davier, Joachim M. Buhmann, Hans R. Heinimann,

European Journal of Engineering Education, 43

Research Collection