Christian F. Baumgartner | DeepAI

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∙ 08/04/2023

Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction

MRI reconstruction techniques based on deep learning have led to unprece...

0 Paul Fischer, et al. ∙

∙ 07/23/2023

Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?

While deep neural network models offer unmatched classification performa...

0 Susu Sun, et al. ∙

∙ 03/08/2023

Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images

Distribution shifts remain a fundamental problem for the safe applicatio...

0 Lisa M. Koch, et al. ∙

∙ 02/06/2023

Studying Therapy Effects and Disease Outcomes in Silico using Artificial Counterfactual Tissue Samples

Understanding the interactions of different cell types inside the immune...

0 Martin Paulikat, et al. ∙

∙ 01/09/2023

Multiscale Metamorphic VAE for 3D Brain MRI Synthesis

Generative modeling of 3D brain MRIs presents difficulties in achieving ...

0 Jaivardhan Kapoor, et al. ∙

∙ 08/05/2022

Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations

Deep Learning (DL) methods have shown promising results for solving ill-...

0 Jan Nikolas Morshuis, et al. ∙

∙ 09/30/2020

Sampling possible reconstructions of undersampled acquisitions in MR imaging

Undersampling the k-space during MR acquisitions saves time, however res...

8 Kerem C. Tezcan, et al. ∙

∙ 07/09/2020

Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation

Supervised learning-based segmentation methods typically require a large...

53 Krishna Chaitanya, et al. ∙

∙ 01/31/2020

Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control

Tissue characterisation with CMR parametric mapping has the potential to...

9 Esther Puyol-Antón, et al. ∙

∙ 06/14/2019

A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation

One of the key drawbacks of 3D convolutional neural networks for segment...

0 Robin Brügger, et al. ∙

∙ 06/07/2019

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

Segmentation of anatomical structures and pathologies is inherently ambi...

4 Christian F. Baumgartner, et al. ∙

∙ 07/24/2018

Combining Heterogeneously Labeled Datasets For Training Segmentation Networks

Accurate segmentation of medical images is an important step towards ana...

2 Jana Kemnitz, et al. ∙

∙ 07/12/2018

Learning to Segment Medical Images with Scribble-Supervision Alone

Semantic segmentation of medical images is a crucial step for the quanti...

6 Yigit B. Can, et al. ∙

∙ 04/24/2018

Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks

Measurement of head biometrics from fetal ultrasonography images is of k...

6 Matthew Sinclair, et al. ∙

∙ 11/30/2017

MR image reconstruction using the learned data distribution as prior

MR image reconstruction from undersampled data exploits priors to compen...

0 Kerem C. Tezcan, et al. ∙

∙ 11/24/2017

Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an imp...

0 Christian F. Baumgartner, et al. ∙

∙ 09/13/2017

An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

Accurate segmentation of the heart is an important step towards evaluati...

0 Christian F. Baumgartner, et al. ∙

∙ 12/16/2016

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultras...

0 Christian F. Baumgartner, et al. ∙

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