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Found 8 papers, 5 papers with code
A 3D generative model of pathological multi-modal MR images and segmentations
The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies.
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
To address this issue, we propose a novel approach that leverages a state-of-the-art 3D brain generative model, the latent diffusion model (LDM) trained on UK BioBank, to increase the resolution of clinical MRI scans.
Unsupervised 3D out-of-distribution detection with latent diffusion models
We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs).
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself.
Can segmentation models be trained with fully synthetically generated data?
Data augmentation is often used to artificially increase the variability in the data distribution and improve model generalisability.
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.
Transformer-based out-of-distribution detection for clinically safe segmentation
We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD.
Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic.
