Search Results for author:
Found 17 papers, 4 papers with code
Learning to synthesise the ageing brain without longitudinal data
Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable).
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections.
CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance.
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances.
A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to Multi-Modality Image Registration
In this paper we describe improvements to the particle swarm optimizer (PSO) made by inclusion of an unscented Kalman filter to guide particle motion.
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway.
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts.
Annealing Genetic GAN for Minority Oversampling
In particular, the generator uses different training strategies to generate multiple offspring and retain the best.
MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis
In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms.
Is attention all you need in medical image analysis? A review
Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers.
Explainable unsupervised multi-modal image registration using deep networks
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities').
