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Found 43 papers, 16 papers with code
Framework to generate perfusion map from CT and CTA images in patients with acute ischemic stroke: A longitudinal and cross-sectional study
Stroke is a leading cause of disability and death.
RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology.
Compressed representation of brain genetic transcription
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space.
Computational limits to the legibility of the imaged human brain
It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.
The minimal computational substrate of fluid intelligence
The quantification of cognitive powers rests on identifying a behavioural task that depends on them.
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).
Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols
To overcome these limitations, we propose Patch-CNN, a neural network with a minimal (non-voxel-wise) convolutional kernel (3$\times$3$\times$3).
Deep Variational Lesion-Deficit Mapping
Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate.
Individualized prescriptive inference in ischaemic stroke
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials.
Brain tumour genetic network signatures of survival
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology.
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.
Focal and Connectomic Mapping of Transiently Disrupted Brain Function
Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.
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.
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.
Representational Ethical Model Calibration
Equity is widely held to be fundamental to the ethics of healthcare.
How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?
A possible consequence of the lack of rotational equivariance is that the training dataset must contain a diverse range of microstucture orientations.
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid.
Brain tumour segmentation with incomplete imaging data
This is a task contemporary machine learning could facilitate, especially in neuroimaging, but its ability to deal with incomplete data common in real world clinical practice remains unknown.
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.
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.
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.
Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion
Models of human motion commonly focus either on trajectory prediction or action classification but rarely both.
Deep forecasting of translational impact in medical research
We show that citations are only moderately predictive of translational impact as judged by inclusion in patents, guidelines, or policy documents.
Value-aware transformers for 1.5d data
Sparse sequential highly-multivariate data of the form characteristic of hospital in-patient investigation and treatment poses a considerable challenge for representation learning.
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.
An MRF-UNet Product of Experts for Image Segmentation
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data.
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.
Generative Model-Enhanced Human Motion Prediction
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD).
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.
Flexible Bayesian Modelling for Nonlinear Image Registration
We describe a diffeomorphic registration algorithm that allows groups of images to be accurately aligned to a common space, which we intend to incorporate into the SPM software.
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.
Unsupervised Videographic Analysis of Rodent Behaviour
Animal behaviour is complex and the amount of data in the form of video, if extracted, is copious.
A Tool for Super-Resolving Multimodal Clinical MRI
The model-driven nature of the approach means that no type of training is needed for applicability to the diversity of MR contrasts present in a clinical context.
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
Empirical Bayesian Mixture Models for Medical Image Translation
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications.
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low.
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast.
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference.
PIMMS: Permutation Invariant Multi-Modal Segmentation
In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality.
VTrails: Inferring Vessels with Geodesic Connectivity Trees
The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale.
NiftyNet: a deep-learning platform for medical imaging
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
