research
∙
08/23/2023
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
High-resolution (HR) MRI scans obtained from research-grade medical cent...
share
research
∙
07/27/2023
Generative AI for Medical Imaging: extending the MONAI Framework
Recent advances in generative AI have brought incredible breakthroughs i...
research
∙
07/07/2023
Unsupervised 3D out-of-distribution detection with latent diffusion models
Methods for out-of-distribution (OOD) detection that scale to 3D data ar...
research
∙
11/14/2022
Denoising Diffusion Models for Out-of-Distribution Detection
Out-of-distribution detection is crucial to the safe deployment of machi...
research
∙
09/17/2022
Can segmentation models be trained with fully synthetically generated data?
In order to achieve good performance and generalisability, medical image...
research
∙
09/15/2022
Brain Imaging Generation with Latent Diffusion Models
Deep neural networks have brought remarkable breakthroughs in medical im...
research
∙
09/07/2022
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Human anatomy, morphology, and associated diseases can be studied using ...
research
∙
06/07/2022
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Deep generative models have emerged as promising tools for detecting arb...
research
∙
05/21/2022
Transformer-based out-of-distribution detection for clinically safe segmentation
In a clinical setting it is essential that deployed image processing sys...
research
∙
03/03/2021
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans
An important goal of medical imaging is to be able to precisely detect p...
research
∙
06/15/2020
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Feature attribution (FA), or the assignment of class-relevance to differ...
research
∙
02/13/2020
