8 citations · 16 across the 7 of their papers we have counts for
9 papers
Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior
Hugues Roy, Reuben Dorent, Ninon Burgos
Unsupervised anomaly detection (UAD) aims to localize abnormal regions in medical scans without pixel-level annotations. A typical strategy seeks to reconstruct a pseudo-healthy im…
Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection
Agathe Senellart, Maëlys Solal, Stéphanie Allassonnière +1
Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are oft…
Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement
Francesco Galati, Daniele Falcetta, Rosa Cortese +3
The intricate morphology of brain vessels poses significant challenges for automatic segmentation models, which usually focus on a single imaging modality. However, accurately trea…
Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease
Hugues Roy, Reuben Dorent, Ninon Burgos
Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological d…
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23
Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
Evangelia Christodoulou, Annika Reinke, Rola Houhou +19
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Ravi Hassanaly, Camille Brianceau, Maëlys Solal +2
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel…
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Maëlys Solal, Ravi Hassanaly, Ninon Burgos
Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on b…
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
Francesco Galati, Daniele Falcetta, Rosa Cortese +4
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality…