Julia A. Schnabel

research

∙ 09/05/2023

A skeletonization algorithm for gradient-based optimization

The skeleton of a digital image is a compact representation of its topol...

0 Martin J. Menten, et al. ∙

research

∙ 08/26/2023

Bias in Unsupervised Anomaly Detection in Brain MRI

Unsupervised anomaly detection methods offer a promising and flexible al...

0 Cosmin I. Bercea, et al. ∙

research

∙ 08/17/2023

ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space

Motion-resolved reconstruction for abdominal magnetic resonance imaging ...

0 Veronika Spieker, et al. ∙

research

∙ 07/24/2023

Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains

Deep generative models have emerged as influential instruments for data ...

0 Maxime Di Folco, et al. ∙

research

∙ 05/31/2023

Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models

The introduction of diffusion models in anomaly detection has paved the ...

0 Cosmin I. Bercea, et al. ∙

research

∙ 05/11/2023

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

Motion represents one of the major challenges in magnetic resonance imag...

0 Veronika Spieker, et al. ∙

research

∙ 03/15/2023

Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection

Early and accurate disease detection is crucial for patient management a...

0 Cosmin I. Bercea, et al. ∙

research

∙ 01/23/2023

Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study

Background: Deep learning (DL) can extract predictive and prognostic bio...

0 Sophia J. Wagner, et al. ∙

research

∙ 09/28/2022

Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging

Flow analysis carried out using phase contrast cardiac magnetic resonanc...

28 Emily Chan, et al. ∙

research

∙ 06/08/2022

What do we learn? Debunking the Myth of Unsupervised Outlier Detection

Even though auto-encoders (AEs) have the desirable property of learning ...

21 Cosmin I. Bercea, et al. ∙

research

∙ 05/02/2022

A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Cine cardiac magnetic resonance (CMR) imaging is considered the gold sta...

32 Inês P. Machado, et al. ∙

research

∙ 09/22/2021

The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images

Domain shift refers to the difference in the data distribution of two da...

27 Devran Ugurlu, et al. ∙

research

∙ 09/16/2021

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

Cine cardiac MRI is routinely acquired for the assessment of cardiac hea...

13 Ines Machado, et al. ∙

research

∙ 08/11/2021

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification

Using publicly available data to determine the performance of methodolog...

8 Vasileios Baltatzis, et al. ∙

research

∙ 08/10/2021

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data

Convolutional Neural Networks (CNNs) are widely used for image classific...

16 Vasileios Baltatzis, et al. ∙

research

∙ 06/18/2021

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is comm...

21 Lei Li, et al. ∙

research

∙ 06/16/2021

AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs

Left atrial (LA) segmentation from late gadolinium enhanced magnetic res...

13 Lei Li, et al. ∙

research

∙ 08/27/2020

Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

Deep learning (DL)-based models have demonstrated good performance in me...

17 Lei Li, et al. ∙

research

∙ 08/11/2020

AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information

Left atrial (LA) and atrial scar segmentation from late gadolinium enhan...

9 Lei Li, et al. ∙

research

∙ 07/13/2020

Screen Tracking for Clinical Translation of Live Ultrasound Image Analysis Methods

Ultrasound (US) imaging is one of the most commonly used non-invasive im...

0 Simona Treivase, et al. ∙

research

∙ 06/23/2020

Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention

We propose an end-to-end deep neural network (DNN) which can simultaneou...

6 Lei Li, et al. ∙

research

∙ 10/11/2019

Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

Segmenting anatomical structures in medical images has been successfully...

34 Ilkay Oksuz, et al. ∙

research

∙ 10/11/2019

Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentatio

Segmenting anatomical structures in medical images has been successfully...

27 Ilkay Oksuz, et al. ∙

research

∙ 10/04/2019

A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology

We introduce a method for training neural networks to perform image or v...

86 James R. Clough, et al. ∙

research

∙ 09/24/2019

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

AUTOMAP is a promising generalized reconstruction approach, however, it ...

30 Jo Schlemper, et al. ∙

research

∙ 09/23/2019

Model-Based and Data-Driven Strategies in Medical Image Computing

Model-based approaches for image reconstruction, analysis and interpreta...

30 Daniel Rueckert, et al. ∙

research

∙ 06/14/2019

Global and Local Interpretability for Cardiac MRI Classification

Deep learning methods for classifying medical images have demonstrated i...

4 James R. Clough, et al. ∙

research

∙ 06/12/2019

Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space

In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corru...

3 lkay Oksuz, et al. ∙

research

∙ 05/17/2019

Mechanically Powered Motion Imaging Phantoms: Proof of Concept

Motion imaging phantoms are expensive, bulky and difficult to transport ...

0 Alberto Gomez, et al. ∙

research

∙ 03/05/2019

FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms

We present a new approach to diffeomorphic non-rigid registration of med...

8 Daniel Grzech, et al. ∙

research

∙ 01/29/2019

Explicit topological priors for deep-learning based image segmentation using persistent homology

We present a novel method to explicitly incorporate topological prior kn...

0 James R. Clough, et al. ∙

research

∙ 12/19/2018

Magnetic Resonance Fingerprinting using Recurrent Neural Networks

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitativ...

0 Ilkay Oksuz, et al. ∙

research

∙ 10/29/2018

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Good quality of medical images is a prerequisite for the success of subs...

2 lkay Oksuz, et al. ∙

research

∙ 08/15/2018

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automatio...

2 Ilkay Oksuz, et al. ∙

research

∙ 08/02/2018

Weakly Supervised Localisation for Fetal Ultrasound Images

This paper addresses the task of detecting and localising fetal anatomic...

0 Nicolas Toussaint, et al. ∙

research

∙ 07/27/2018

Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

The availability of large scale databases containing imaging and non-ima...

0 Esther Puyol-Antón, et al. ∙

research

∙ 07/19/2018

EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

Ultrasound (US) is the most widely used fetal imaging technique. However...

0 Bishesh Khanal, et al. ∙

research

∙ 06/01/2018

Oversegmenting Graphs

We propose a novel method to adapt a graph to image data. The method dri...

4 Alberto Gomez, et al. ∙

research

∙ 05/26/2017

Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks

Vasculature is known to be of key biological significance, especially in...

0 Russell Bates, et al. ∙

research

∙ 04/18/2016

Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is...

0 Benjamin Irving, et al. ∙

Julia A. Schnabel

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