
Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks
Glioma is a common malignant brain tumor with distinct survival among pa...
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Adaptive unsupervised learning with enhanced feature representation for intratumor partitioning and survival prediction for glioblastoma
Glioblastoma is profoundly heterogeneous in regional microstructure and ...
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Image reconstruction in lightsheet microscopy: spatially varying deconvolution and mixed noise
We study the problem of deconvolution for lightsheet microscopy, where ...
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LaplaceNet: A Hybrid EnergyNeural Model for Deep SemiSupervised Classification
Semisupervised learning has received a lot of recent attention as it al...
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HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Superpixels serve as a powerful preprocessing tool in many computer visi...
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Endtoend reconstruction meets datadriven regularization for inverse problems
We propose an unsupervised approach for learning endtoend reconstructi...
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CAFLOW: Conditional Autoregressive Flows
We introduce CAFLOW, a new diverse imagetoimage translation model that...
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Advances in Artificial Intelligence to Reduce Polyp Miss Rates during Colonoscopy
BACKGROUND AND CONTEXT: Artificial intelligence has the potential to aid...
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An endtoend Optical Character Recognition approach for ultralowresolution printed text images
Some historical and more recent printed documents have been scanned or s...
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Semisupervised Superpixelbased MultiFeature Graph Learning for Hyperspectral Image Data
Graphs naturally lend themselves to model the complexities of Hyperspect...
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Adversarially learned iterative reconstruction for imaging inverse problems
In numerous practical applications, especially in medical image reconstr...
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Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Wasserstein GANs are based on the idea of minimising the Wasserstein dis...
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Equivariant neural networks for inverse problems
In recent years the use of convolutional layers to encode an inductive b...
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Depthwise Separable Convolutions Allow for Fast and MemoryEfficient Spectral Normalization
An increasing number of models require the control of the spectral norm ...
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A Mixed Focal Loss Function for Handling Class Imbalanced Medical Image Segmentation
Automatic segmentation methods are an important advancement in medical i...
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ExpectationMaximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma
We present an ExpectationMaximization (EM) Regularized Deep Learning (E...
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Beyond Finetuning: Classifying High Resolution Mammograms using FunctionPreserving Transformations
The task of classifying mammograms is very challenging because the lesio...
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Bayesian optimization assisted unsupervised learning for efficient intratumor partitioning in MRI and survival prediction for glioblastoma patients
Glioblastoma is profoundly heterogeneous in microstructure and vasculatu...
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A ThreeStage SelfTraining Framework for SemiSupervised Semantic Segmentation
Semantic segmentation has been widely investigated in the community, in ...
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Contrastive Registration for Unsupervised Medical Image Segmentation
Medical image segmentation is a relevant task as it serves as the first ...
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Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
The Magnetic Resonance Imaging (MRI) processing chain starts with a crit...
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GraphXCOVID: Explainable Deep Graph Diffusion PseudoLabelling for Identifying COVID19 on Chest Xrays
Can one learn to diagnose COVID19 under extreme minimal supervision? Si...
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A Linear Transportation L^p Distance for Pattern Recognition
The transportation L^p distance, denoted TL^p, has been proposed as a ge...
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Machine learning for COVID19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review
Background: Machine learning methods offer great potential for fast and ...
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Unsupervised Image Restoration Using Partially Linear Denoisers
Deep neural network based methods are the state of the art in various im...
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Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problem...
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Scanning electron diffraction tomography of strain
Strain engineering is used to obtain desirable materials properties in a...
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Art Speaks Maths, Maths Speaks Art
Our interdisciplinary team Mathematics for Applications in Cultural Heri...
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Ground Truth Free Denoising by Optimal Transport
We present a learned unsupervised denoising method for arbitrary types o...
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Deeply Learned Spectral Total Variation Decomposition
Nonlinear spectral decompositions of images based on onehomogeneous fu...
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SLICUAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs
Logged forests cover four million square kilometres of the tropics and r...
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Structure preserving deep learning
Over the past few years, deep learning has risen to the foreground as a ...
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Unsupervised clustering of Roman pottery profiles from their SSAE representation
In this paper we introduce the ROman COmmonware POTtery (ROCOPOT) databa...
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Multitask deep learning for image segmentation using recursive approximation tasks
Fully supervised deep neural networks for segmentation usually require a...
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On Learned Operator Correction
We discuss the possibility to learn a datadriven explicit model correct...
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iUNets: Fully invertible UNets with Learnable Up and Downsampling
UNets have been established as a standard neural network design archite...
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Two Cycle Learning: Clustering Based Regularisation for Deep SemiSupervised Classification
This works addresses the challenge of classification with minimal annota...
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Equilibria of an anisotropic nonlocal interaction equation: Analysis and numerics
In this paper, we study the equilibria of an anisotropic, nonlocal aggre...
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Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
Indirect image registration is a promising technique to improve image re...
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Total Variation Regularisation with Spatially Variable Lipschitz Constraints
We introduce a first order Total Variation type regulariser that decompo...
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Dynamic Spectral Residual Superpixels
We consider the problem of segmenting an image into superpixels in the c...
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Variational Osmosis for Nonlinear Image Fusion
We propose a new variational model for nonlinear image fusion. Our appro...
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PDEInspired Algorithms for SemiSupervised Learning on Point Clouds
Given a data set and a subset of labels the problem of semisupervised l...
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Variational MultiTask MRI Reconstruction: Joint Reconstruction, Registration and SuperResolution
Motion degradation is a central problem in Magnetic Resonance Imaging (M...
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GraphX^NET Chest XRay Classification Under Extreme Minimal Supervision
The task of classifying Xray data is a problem of both theoretical and ...
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A multitask Unet for segmentation with lazy labels
The need for labour intensive pixelwise annotation is a major limitatio...
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Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisatio...
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Beyond Supervised Classification: Extreme Minimal Supervision with the Graph 1Laplacian
We consider the task of classifying when an extremely reduced amount of ...
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On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Recent studies on the adversarial vulnerability of neural networks have ...
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Deep learning as optimal control problems: models and numerical methods
We consider recent work of Haber and Ruthotto 2017 and Chang et al. 2018...
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CarolaBibiane Schönlieb
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Reader in Applied and Computational Analysis, Head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Director of the Cantab Capital Institute for the Mathematics of Information, CoDirector of the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, Fellow of Jesus College, Cambridge, coleader of the IMAGES network