2 citations · 11 across the 22 of their papers we have counts for
10 papers · 1 filter
NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
Cosmin I. Bercea, Jun Li, Philipp Raffler +12
In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems fr…
Denoising Diffusion Models for Anomaly Localization in Medical Images
Cosmin I. Bercea, Philippe C. Cattin, Julia A. Schnabel +1
This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their app…
MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI
Malek Ben Alaya, Daniel M. Lang, Benedikt Wiestler +2
Denoising diffusion probabilistic models enable high-fidelity image synthesis and editing. In biomedicine, these models facilitate counterfactual image editing, producing pairs of…
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound
Hanna Mykula, Lisa Gasser, Silvia Lobmaier +3
Ultrasonography is an essential tool in mid-pregnancy for assessing fetal development, appreciated for its non-invasive and real-time imaging capabilities. Yet, the interpretation…
Unsupervised Analysis of Alzheimer's Disease Signatures using 3D Deformable Autoencoders
Mehmet Yigit Avci, Emily Chan, Veronika Zimmer +4
With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the dis…
Interpretable Representation Learning of Cardiac MRI via Attribute Regularization
Maxime Di Folco, Cosmin I. Bercea, Emily Chan +1
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered…
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approach…
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
The increasing complexity of medical imaging data underscores the need for advanced anomaly detection methods to automatically identify diverse pathologies. Current methods face ch…
Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification
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. In this paper, we propose a novel interpretable…
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
lkay Oksuz, James Clough, Bram Ruijsink +7
In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose…