171 citations · 267 across the 20 of their papers we have counts for
13 papers · 1 filter
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks d…
Studying Therapy Effects and Disease Outcomes in Silico using Artificial Counterfactual Tissue Samples
Martin Paulikat, Christian M. Schürch, Christian F. Baumgartner
Understanding the interactions of different cell types inside the immune tumor microenvironment (iTME) is crucial for the development of immunotherapy treatments as well as for pre…
A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images
Christoph Leitner, Robert Jarolim, Bernhard Englmair +9
Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such a…
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
One of the key drawbacks of 3D convolutional neural networks for segmentation is their memory footprint, which necessitates compromises in the network architecture in order to fit…
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth +3
Accurate segmentation of medical images is an important step towards analyzing and tracking disease related morphological alterations in the anatomy. Convolutional neural networks…
Learning to Segment Medical Images with Scribble-Supervision Alone
Yigit B. Can, Krishna Chaitanya, Basil Mustafa +3
Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation al…
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Matthew Sinclair, Christian F. Baumgartner, Jacqueline Matthew +9
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevan…
MR image reconstruction using deep density priors
Kerem C. Tezcan, Christian F. Baumgartner, Roger Luechinger +2
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) prov…
Visual Feature Attribution using Wasserstein GANs
Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan +2
Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localis…
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Christian F. Baumgartner, Lisa M. Koch, Marc Pollefeys +1
Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV)…
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig +8
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performan…
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew +5
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding…
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Lisa M. Koch, Martin Rajchl, Wenjia Bai +5
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability…