3 citations · 3 across the 7 of their papers we have counts for
7 papers
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
Sergio Naval Marimont, Vasilis Siomos, Matthew Baugh +3
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free.…
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos +3
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free…
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Sergio Naval Marimont, Vasilis Siomos, Giacomo Tarroni
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established app…
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
Sergio Naval Marimont, Giacomo Tarroni
The detection and localization of anomalies is one important medical image analysis task. Most commonly, Computer Vision anomaly detection approaches rely on manual annotations tha…
Implicit U-Net for volumetric medical image segmentation
Sergio Naval Marimont, Giacomo Tarroni
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose th…
Implicit field learning for unsupervised anomaly detection in medical images
Sergio Naval Marimont, Giacomo Tarroni
We propose a novel unsupervised out-of-distribution detection method for medical images based on implicit fields image representations. In our approach, an auto-decoder feed-forwar…
Anomaly detection through latent space restoration using vector-quantized variational autoencoders
Sergio Naval Marimont, Giacomo Tarroni
We propose an out-of-distribution detection method that combines density and restoration-based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ-VAE mod…