Search Results for author: Julia A. Schnabel

Found 50 papers, 18 papers with code

Multi-Image Visual Question Answering for Unsupervised Anomaly Detection

no code implementations11 Apr 2024 Jun Li, Cosmin I. Bercea, Philip Müller, Lina Felsner, Suhwan Kim, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel

To the best of our knowledge, we are the first to leverage a language model for unsupervised anomaly detection, for which we construct a dataset with different questions and answers.

Language Modelling Question Answering +2

Diffusion Models with Implicit Guidance for Medical Anomaly Detection

1 code implementation13 Mar 2024 Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel

Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents.

Specificity Unsupervised Anomaly Detection

Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI

1 code implementation13 Mar 2024 Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, Julia A. Schnabel

We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities.

Towards Universal Unsupervised Anomaly Detection in Medical Imaging

1 code implementation19 Jan 2024 Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel

Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies.

Unsupervised Anomaly Detection

Low-resource finetuning of foundation models beats state-of-the-art in histopathology

1 code implementation9 Jan 2024 Benedikt Roth, Valentin Koch, Sophia J. Wagner, Julia A. Schnabel, Carsten Marr, Tingying Peng

Recently, foundation models in computer vision showed that leveraging huge amounts of data through supervised or self-supervised learning improves feature quality and generalizability for a variety of tasks.

Self-Supervised Learning Weakly-supervised Learning +1

Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification

1 code implementation14 Dec 2023 Maxime Di Folco, Cosmin I. Bercea, Julia A. Schnabel

Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models.


Influence of Prompting Strategies on Segment Anything Model (SAM) for Short-axis Cardiac MRI segmentation

no code implementations14 Dec 2023 Josh Stein, Maxime Di Folco, Julia A. Schnabel

We conduct a comprehensive investigation of the impact of different prompting strategies (including bounding boxes, positive points, negative points, and their combinations) on segmentation performance.

MRI segmentation Segmentation +1

Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks

1 code implementation14 Nov 2023 Anna Reithmeir, Julia A. Schnabel, Veronika A. Zimmer

In particular, we adapt the HyperMorph framework to learn the effect of the two elasticity parameters of the linear elastic regularizer.

Image Registration Medical Image Registration

A skeletonization algorithm for gradient-based optimization

1 code implementation ICCV 2023 Martin J. Menten, Johannes C. Paetzold, Veronika A. Zimmer, Suprosanna Shit, Ivan Ezhov, Robbie Holland, Monika Probst, Julia A. Schnabel, Daniel Rueckert

Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.


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

1 code implementation17 Aug 2023 Veronika Spieker, Wenqi Huang, Hannah Eichhorn, Jonathan Stelter, Kilian Weiss, Veronika A. Zimmer, Rickmer F. Braren, Dimitrios C. Karampinos, Kerstin Hammernik, Julia A. Schnabel

Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling artefacts.

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

no code implementations24 Jul 2023 Maxime Di Folco, Cosmin Bercea, Julia A. Schnabel

In this work, we propose the Attributed Soft Introspective VAE (Attri-SIVAE) by incorporating an attribute regularized loss, into the Soft-Intro VAE framework.

Attribute Image Generation

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

no code implementations11 May 2023 Veronika Spieker, Hannah Eichhorn, Kerstin Hammernik, Daniel Rueckert, Christine Preibisch, Dimitrios C. Karampinos, Julia A. Schnabel

To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials.

Physics-Aware Motion Simulation for T2*-Weighted Brain MRI

1 code implementation20 Mar 2023 Hannah Eichhorn, Kerstin Hammernik, Veronika Spieker, Samira M. Epp, Daniel Rueckert, Christine Preibisch, Julia A. Schnabel

As T2*-weighted MRI is highly sensitive to motion-related changes in magnetic field inhomogeneities, it is of utmost importance to include physics information in the simulation.

Line Detection

3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI

no code implementations10 Mar 2023 Daniel M. Lang, Eli Schwartz, Cosmin I. Bercea, Raja Giryes, Julia A. Schnabel

This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition.

Anomaly Detection Lesion Detection

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

no code implementations28 Sep 2022 Emily Chan, Ciaran O'Hanlon, Carlota Asegurado Marquez, Marwenie Petalcorin, Jorge Mariscal-Harana, Haotian Gu, Raymond J. Kim, Robert M. Judd, Phil Chowienczyk, Julia A. Schnabel, Reza Razavi, Andrew P. King, Bram Ruijsink, Esther Puyol-Antón

Flow analysis carried out using phase contrast cardiac magnetic resonance imaging (PC-CMR) enables the quantification of important parameters that are used in the assessment of cardiovascular function.

Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View

1 code implementation29 Jun 2022 Veronika A. Zimmer, Alberto Gomez, Emily Skelton, Robert Wright, Gavin Wheeler, Shujie Deng, Nooshin Ghavami, Karen Lloyd, Jacqueline Matthew, Bernhard Kainz, Daniel Rueckert, Joseph V. Hajnal, Julia A. Schnabel

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation.

Image Segmentation Multi-Task Learning +3

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

no code implementations8 Jun 2022 Cosmin I. Bercea, Daniel Rueckert, Julia A. Schnabel

We have found that state-of-the-art (SOTA) AEs are either unable to constrain the latent manifold and allow reconstruction of abnormal patterns, or they are failing to accurately restore the inputs from their latent distribution, resulting in blurred or misaligned reconstructions.

Outlier Detection Out of Distribution (OOD) Detection

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

no code implementations16 Sep 2021 Ines Machado, Esther Puyol-Anton, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King

The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.

MRI Reconstruction

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

no code implementations11 Aug 2021 Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loic Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel

Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.

Lung Nodule Classification

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

1 code implementation18 Jun 2021 Lei LI, Veronika A. Zimmer, Julia A. Schnabel, Xiahai Zhuang

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars.


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

no code implementations16 Jun 2021 Lei LI, Veronika A. Zimmer, Julia A. Schnabel, Xiahai Zhuang

Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation.

Domain Generalization Segmentation +2

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

no code implementations27 Aug 2020 Lei Li, Veronika A. Zimmer, Wangbin Ding, Fuping Wu, Liqin Huang, Julia A. Schnabel, Xiahai Zhuang

As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains.

Domain Generalization Image Segmentation +5

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

1 code implementation11 Aug 2020 Lei Li, Veronika A. Zimmer, Julia A. Schnabel, Xiahai Zhuang

In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style.


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

no code implementations23 Jun 2020 Lei Li, Xin Weng, Julia A. Schnabel, Xiahai Zhuang

Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods.


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

no code implementations11 Oct 2019 Ilkay Oksuz, James R. Clough, Bram Ruijsink, Esther Puyol Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A. Schnabel

In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.

Image Reconstruction Image Segmentation +2

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

1 code implementation4 Oct 2019 James R. Clough, Nicholas Byrne, Ilkay Oksuz, Veronika A. Zimmer, Julia A. Schnabel, Andrew P. King

We show that the incorporation of the prior knowledge of the topology of this anatomy improves the resulting segmentations in terms of both the topological accuracy and the Dice coefficient.

Anatomy Image Segmentation +3

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

1 code implementation24 Sep 2019 Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.

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

no code implementations23 Sep 2019 Daniel Rueckert, Julia A. Schnabel

With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis and interpretation.

Image Reconstruction Transfer Learning

Global and Local Interpretability for Cardiac MRI Classification

no code implementations14 Jun 2019 James R. Clough, Ilkay Oksuz, Esther Puyol-Anton, Bram Ruijsink, Andrew P. King, Julia A. Schnabel

Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice.

Classification General Classification +1

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

no code implementations29 Jan 2019 James R. Clough, Ilkay Oksuz, Nicholas Byrne, Julia A. Schnabel, Andrew P. King

We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so.

Image Segmentation Left Ventricle Segmentation +3

Magnetic Resonance Fingerprinting using Recurrent Neural Networks

no code implementations19 Dec 2018 Ilkay Oksuz, Gastao Cruz, James Clough, Aurelien Bustin, Nicolo Fuin, Rene M. Botnar, Claudia Prieto, Andrew P. King, Julia A. Schnabel

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition.

Magnetic Resonance Fingerprinting

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

no code implementations29 Oct 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, James Clough, Gastao Cruz, Aurelien Bustin, Claudia Prieto, Rene Botnar, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.

Data Augmentation General Classification +2

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

no code implementations15 Aug 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.

Data Augmentation Image Quality Assessment +1

Weakly Supervised Localisation for Fetal Ultrasound Images

2 code implementations2 Aug 2018 Nicolas Toussaint, Bishesh Khanal, Matthew Sinclair, Alberto Gomez, Emily Skelton, Jacqueline Matthew, Julia A. Schnabel

This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i. e. without any localisation or segmentation information.

Pose Estimation Segmentation

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

no code implementations27 Jul 2018 Esther Puyol-Anton, Bram Ruijsink, Helene Langet, Mathieu De Craene, Paolo Piro, Julia A. Schnabel, Andrew P. King

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function.

Dimensionality Reduction

Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning

no code implementations1 Jun 2018 Alberto Gomez, Veronika A. Zimmer, Bishesh Khanal, Nicolas Toussaint, Julia A. Schnabel

From the adapted graph, we also propose the computation of a dual graph, which inherits the saliency measure from the adapted graph, and whose edges run along image features, hence producing an oversegmenting graph.

Clustering General Classification +1

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

no code implementations18 Apr 2016 Benjamin Irving, James M Franklin, Bartlomiej W. Papiez, Ewan M Anderson, Ricky A Sharma, Fergus V Gleeson, Sir Michael Brady, Julia A. Schnabel

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice.


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