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Found 9 papers, 5 papers with code
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making.
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis.
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.
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection
In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach with limited training data.
Transformer-based normative modelling for anomaly detection of early schizophrenia
Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0. 82 when assessing the difference between controls and individuals with early-stage schizophrenia.
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.
Brain Imaging Generation with Latent Diffusion Models
In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images.
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.
Neuradicon: operational representation learning of neuroimaging reports
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis.
