no code implementations • 24 Apr 2024 • Stefano Woerner, Arthur Jaques, Christian F. Baumgartner
While the field of medical image analysis has undergone a transformative shift with the integration of machine learning techniques, the main challenge of these techniques is often the scarcity of large, diverse, and well-annotated datasets.
1 code implementation • 4 Dec 2023 • Alexander Frotscher, Jaivardhan Kapoor, Thomas Wolfers, Christian F. Baumgartner
Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions.
1 code implementation • 4 Aug 2023 • Paul Fischer, Thomas Küstner, Christian F. Baumgartner
We demonstrate that our proposed method produces high-quality reconstructions as well as uncertainty quantification that is substantially better calibrated than several strong baselines.
1 code implementation • 23 Jul 2023 • Susu Sun, Lisa M. Koch, Christian F. Baumgartner
Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data.
1 code implementation • 8 Mar 2023 • Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner, Arthur Gretton, Philipp Berens
We formulate subgroup shift detection in the framework of statistical hypothesis testing and show that recent state-of-the-art statistical tests can be effectively applied to subgroup shift detection on medical imaging data.
1 code implementation • 1 Mar 2023 • Susu Sun, Stefano Woerner, Andreas Maier, Lisa M. Koch, Christian F. Baumgartner
Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image.
no code implementations • 6 Feb 2023 • Martin Paulikat, Christian M. Schürch, Christian F. Baumgartner
HMTI technologies can be used to gain insights into the iTME and in particular how the iTME differs for different patient outcome groups of interest (e. g., treatment responders vs. non-responders).
no code implementations • 9 Jan 2023 • Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution.
1 code implementation • 5 Aug 2022 • Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner
Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled $k$-space data.
1 code implementation • 30 Sep 2020 • Kerem C. Tezcan, Neerav Karani, Christian F. Baumgartner, Ender Konukoglu
In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process.
1 code implementation • 9 Jul 2020 • Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Ertunc Erdil, Anton Becker, Olivio Donati, Ender Konukoglu
In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task.
no code implementations • 31 Jan 2020 • Esther Puyol Anton, Bram Ruijsink, Christian F. Baumgartner, Matthew Sinclair, Ender Konukoglu, Reza Razavi, Andrew P. King
The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients.
4 code implementations • 14 Jun 2019 • Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
Increasing network depth led to higher segmentation accuracy while growing the memory footprint only by a very small fraction, thanks to the partially reversible architecture.
3 code implementations • 7 Jun 2019 • Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya, Andreas M. Hötker, Urs J. Muehlematter, Khoschy Schawkat, Anton S. Becker, Olivio Donati, Ender Konukoglu
Segmentation of anatomical structures and pathologies is inherently ambiguous.
no code implementations • 24 Jul 2018 • Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth, Felix Eckstein, Sebastian K. Eder, Ender Konukoglu
In this work we propose a cost function which allows integration of multiple datasets with heterogeneous label subsets into a joint training.
no code implementations • 12 Jul 2018 • Yigit B. Can, Krishna Chaitanya, Basil Mustafa, Lisa M. Koch, Ender Konukoglu, Christian F. Baumgartner
We find that the networks trained on scribbles suffer from a remarkably small degradation in Dice of only 2. 9% (cardiac) and 4. 5% (prostate) with respect to a network trained on full annotations.
no code implementations • 24 Apr 2018 • Matthew Sinclair, Christian F. Baumgartner, Jacqueline Matthew, Wenjia Bai, Juan Cerrolaza Martinez, Yuanwei Li, Sandra Smith, Caroline L. Knight, Bernhard Kainz, Jo Hajnal, Andrew P. King, Daniel Rueckert
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses.
no code implementations • 30 Nov 2017 • Kerem C. Tezcan, Christian F. Baumgartner, Roger Luechinger, Klaas P. Pruessmann, Ender Konukoglu
Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction.
3 code implementations • CVPR 2018 • Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan, Jia Xi Ang, Ender Konukoglu
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 localisation to understanding hidden effects in the data.
1 code implementation • 13 Sep 2017 • Christian F. Baumgartner, Lisa M. Koch, Marc Pollefeys, Ender Konukoglu
Accurate segmentation of the heart is an important step towards evaluating cardiac function.
2 code implementations • 16 Dec 2016 • Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, Daniel Rueckert
In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box.
no code implementations • 29 Apr 2016 • Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.