68 citations · 213 across the 20 of their papers we have counts for
6 papers · 1 filter
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine lea…
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Alexander Immer, Christoph Schultheiss, Julia E. Vogt +3
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect can be written as a function of the cause and a noise source independe…
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiograph…
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models
Alain Ryser, Laura Manduchi, Fabian Laumer +3
We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a…
How Robust is Unsupervised Representation Learning to Distribution Shift?
Yuge Shi, Imant Daunhawer, Julia E. Vogt +2
The robustness of machine learning algorithms to distributions shift is primarily discussed in the context of supervised learning (SL). As such, there is a lack of insight on the r…
Learning Group Importance using the Differentiable Hypergeometric Distribution
Thomas M. Sutter, Laura Manduchi, Alain Ryser +1
Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes…