Search Results for author:
Found 89 papers, 32 papers with code
Classification of datasets with imputed missing values: does imputation quality matter?
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
Learned reconstruction with convergence guarantees
In recent years, deep learning has achieved remarkable empirical success for image reconstruction.
Unsupervised Learning of the Total Variation Flow
Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, a neural network approach to compute the solution of the TV flow given an initial image and a time instance.
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
Operator Sketching for Deep Unrolling Networks
In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching.
Multi-modal learning for predicting the genotype of glioma
In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.
Unsupervised Clustering of Roman Potsherds via Variational Autoencoders
In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds.
PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.
Predicting conversion of mild cognitive impairment to Alzheimer's disease
In this study, we develop a self-supervised contrastive learning approach to generate structural brain networks from routine anatomical MRI under the guidance of diffusion MRI.
Mutual Contrastive Learning to Disentangle Whole Slide Image Representations for Glioma Grading
We first design a mutual learning scheme to jointly optimize the model training based on FFPE and frozen sections.
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework.
Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone.
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Optimization problems with continuous data appear in, e. g., robust machine learning, functional data analysis, and variational inference.
Conditional Image Generation with Score-Based Diffusion Models
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation
Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.
Focal Attention Networks: optimising attention for biomedical image segmentation
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians.
Learning convex regularizers satisfying the variational source condition for inverse problems
Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades.
Invertible Learned Primal-Dual
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction.
Stochastic Primal-Dual Deep Unrolling
We develop a stochastic (ordered-subsets) variant of the classical learned primal-dual (LPD), which is a state-of-the-art unrolling network for tomographic image reconstruction.
StyleGAN-induced data-driven regularization for inverse problems
Our proposed approach, which we refer to as learned Bayesian reconstruction with generative models (L-BRGM), entails joint optimization over the style-code and the input latent code, and enhances the expressive power of a pre-trained StyleGAN2 generator by allowing the style-codes to be different for different generator layers.
Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks
Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients.
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.
LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification
Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect.
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously.
End-to-end reconstruction meets data-driven regularization for inverse problems
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems.
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0. 878 and mean IoU of 0. 809, a 14% and 15% improvement over the previous state-of-the-art results of 0. 768 and 0. 702, respectively.
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images
Our experiments have shown that it is possible to perform OCR on 60 dpi scanned images of English text, which is a significantly lower resolution than the state-of-the-art, and we achieved a mean character level accuracy (CLA) of 99. 7% and word level accuracy (WLA) of 98. 9% across a set of about 1000 pages of 60 dpi text in a wide range of fonts.
Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data
In this work, we present a novel framework for the classification of HSI data in light of a very limited amount of labelled data, inspired by multi-view graph learning and graph signal processing.
Adversarially learned iterative reconstruction for imaging inverse problems
In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning.
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.
Equivariant neural networks for inverse problems
In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach.
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network.
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions.
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma
EMReDL showed to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration.
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes.
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
The key idea of our technique is the extraction of the pseudo-masks statistical information to decrease uncertainty in the predicted probability whilst enforcing segmentation consistency in a multi-task fashion.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.
Contrastive Registration for Unsupervised Medical Image Segmentation
Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
In this work we propose a framework for joint optimization of the MRI reconstruction and lossy compression, producing compressed representations of medical images that achieve improved trade-offs between quality and bit-rate.
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
A Linear Transportation $\mathrm{L}^p$ Distance for Pattern Recognition
The transportation $\mathrm{L}^p$ distance, denoted $\mathrm{TL}^p$, has been proposed as a generalisation of Wasserstein $\mathrm{W}^p$ distances motivated by the property that it can be applied directly to colour or multi-channelled images, as well as multivariate time-series without normalisation or mass constraints.
Unsupervised Image Restoration Using Partially Linear Denoisers
The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
Ground Truth Free Denoising by Optimal Transport
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals.
Deeply Learned Spectral Total Variation Decomposition
To the best of our knowledge, this is the first approach towards learning a non-linear spectral decomposition of images.
SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs
To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia.
Structure preserving deep learning
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks.
Unsupervised clustering of Roman pottery profiles from their SSAE representation
The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem: profile similarities are thus explored via the hierarchical clustering of non-linear features learned in the latent representation space of a stacked sparse autoencoder (SSAE) network, unveiling new profile matches.
Multi-task deep learning for image segmentation using recursive approximation tasks
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.
On Learned Operator Correction in Inverse Problems
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.
SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization
We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems.
Image Deconvolution
Stochastic Optimization
Optimization and Control
90C26
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently.
Total Variation Regularisation with Spatially Variable Lipschitz Constraints
We introduce a first order Total Variation type regulariser that decomposes a function into a part with a given Lipschitz constant (which is also allowed to vary spatially) and a jump part.
Numerical Analysis Numerical Analysis 65J20, 65J22, 68U10, 94A08
Dynamic Spectral Residual Superpixels
We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.
Variational Osmosis for Non-linear Image Fusion
We propose a new variational model for non-linear image fusion.
A multi-task U-net for segmentation with lazy labels
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds
In this paper we extend the labels by minimising the constrained discrete $p$-Dirichlet energy.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
Learning to segment microscopy images with lazy labels
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete".
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge.
Multi-class Classification
Semi-Supervised Image Classification
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts.
Deep learning as optimal control problems: models and numerical methods
We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation.
Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
From these three-dimensional crowns, we are able to measure individual tree biomass.
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.
A total variation based regularizer promoting piecewise-Lipschitz reconstructions
We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially).
Higher-Order Total Directional Variation. Part I: Imaging Applications
We introduce a new class of higher-order total directional variation regularizers.
Numerical Analysis 47A52, 49M30, 49N45, 65J22, 94A08
Anisotropic osmosis filtering for shadow removal in images
We present an anisotropic extension of the isotropic osmosis model that has been introduced by Weickert et al.~(Weickert, 2013) for visual computing applications, and we adapt it specifically to shadow removal applications.
Analysis of PDEs 68U10, 94A08, 49K20, 65M06,
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem.
Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning
Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins).
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.
Adversarial Regularizers in Inverse Problems
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods.
Unveiling the invisible - mathematical methods for restoring and interpreting illuminated manuscripts
The last fifty years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering.
Peekaboo - Where are the Objects? Structure Adjusting Superpixels
This paper addresses the search for a fast and meaningful image segmentation in the context of $k$-means clustering.
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality.
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable.
Nonlinear Spectral Image Fusion
In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks.
A Variational Model for Joint Motion Estimation and Image Reconstruction
The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model.
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e. g. a ruler).
Bilevel approaches for learning of variational imaging models
We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space.
The structure of optimal parameters for image restoration problems
The analysis is done on the original -- in image restoration typically non-smooth variational problem -- as well as on a smoothed approximation set in Hilbert space which is the one considered in numerical computations.
