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Found 10 papers, 6 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.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
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).
Denoising diffusion models for out-of-distribution detection
We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs.
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.
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling.
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.
Test-time Unsupervised Domain Adaptation
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain).
Hierarchical brain parcellation with uncertainty
We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree.
Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data
Pegasus-OCT was shown to be able to detect AMD, DME and general anomalies in OCT volumes acquired across multiple independent sites with high performance.
