Search Results for author:
Found 22 papers, 13 papers with code
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)
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.
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions.
Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
We demonstrate that our proposed method produces high-quality reconstructions as well as uncertainty quantification that is substantially better calibrated than several strong baselines.
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
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.
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
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.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
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.
Studying Therapy Effects and Disease Outcomes in Silico using Artificial Counterfactual Tissue Samples
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).
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution.
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations
Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled $k$-space data.
Sampling possible reconstructions of undersampled acquisitions in MR imaging
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.
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
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.
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients.
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
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.
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Segmentation of anatomical structures and pathologies is inherently ambiguous.
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
In this work we propose a cost function which allows integration of multiple datasets with heterogeneous label subsets into a joint training.
Learning to Segment Medical Images with Scribble-Supervision Alone
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.
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 key importance in monitoring the healthy development of fetuses.
MR image reconstruction using deep density priors
Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction.
Visual Feature Attribution using Wasserstein GANs
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.
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Accurate segmentation of the heart is an important step towards evaluating cardiac function.
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
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.
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.
