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

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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

Learn More

Follow Us

Others

2022 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...

2021 Conference Paper

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann,

9th International Conference on Learning Representations (ICLR 2021),

Research Collection

How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way, using a sequential gating-based mechanism that distributes contextual information across 2-D space. We show that augmenting the decoder of a hierarchical VAE by spatial dependency...

2021 Journal Article

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....

Self Supervised Learning Representation Learning Unsupervised Learning Surgical Activity Recognition Deep Learning Probabilistic Modeling
2021 Journal Article

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,...

2021 Journal Article

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...

Acute Coronary Syndromes Inflammation Acute Kidney Injury Hscrp Neutrophil To Lymphocyte Ratio Grace Score
2021 Conference Paper

On maximum-likelihood estimation in the all-or-nothing regime

Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann,

2021 IEEE International Symposium on Information Theory (ISIT 2021),

Research Collection

We study the problem of estimating a rank-1 additive deformation of a Gaussian tensor according to the maximum-likelihood estimator (MLE). The analysis is carried out in the sparse setting, where the underlying signal has a support that scales sublinearly with the total number of dimensions. We show that for Bernoulli distributed signals, the MLE undergoes an all-or-nothing (AoN) phase transition, already established for the minimum mean-square-error estimator (MMSE) in the same problem. The result follows from two main technical points:...

2021 Journal Article

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...

Machine Learning Acute Coronary Syndromes Nt Probnp Age Grace 2.0 Score
2021 Journal Article

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...

Diffusion Mri Brain Tractography Machine Learning Maximum Entropy Inference Algorithm Validation
2020 Working Paper

Statistical and computational thresholds for the planted k-densest sub-hypergraph problem

Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann,

arXiv,

DOI: 10.3929/ethz-b-000456981      Research Collection

Recovery a planted signal perturbed by noise is a fundamental problem in machine learning. In this work, we consider the problem of recovery a planted k-densest sub-hypergraph on h-uniform hypergraphs over n nodes. This fundamental problem appears in different contexts, e.g., community detection, average case complexity, and neuroscience applications. We first observe that it can be viewed as a structural variant of tensor PCA in which the hypergraph parameters k and h determine the structure of the signal to be...

2020 Journal Article

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...

Mitral Valve Segmentation Collaborative Filtering Neural Network