2 citations · 11 across the 22 of their papers we have counts for
11 papers · 1 filter
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…
A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer
Inês P. Machado, Anna Reithmeir, Fryderyk Kogl +13
High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and temporal heterogeneity, typically manifesting at an advanced metastatic stage. A major chall…
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…
Machine Unlearning for Medical Imaging
Reza Nasirigerdeh, Nader Razmi, Julia A. Schnabel +2
Machine unlearning is the process of removing the impact of a particular set of training samples from a pretrained model. It aims to fulfill the "right to be forgotten", which gran…
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…
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
Jun Li, Su Hwan Kim, Philip Müller +5
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the i…
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…
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
Anneliese Riess, Kristian Schwethelm, Johannes Kaiser +4
The gold standard for privacy in machine learning, Differential Privacy (DP), is often interpreted through its guarantees against membership inference. However, translating DP budg…
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…