no code implementations • 8 Apr 2024 • Hong Ye Tan, Ziruo Cai, Marcelo Pereyra, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb
Unsupervised learning is a training approach in the situation where ground truth data is unavailable, such as inverse imaging problems.
no code implementations • 19 Mar 2024 • Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng, Zoe Kourtzi, Carola-Bibiane Schönlieb
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life.
no code implementations • 14 Mar 2024 • Lipei Zhang, Yanqi Cheng, Lihao Liu, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Recent advancements in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information.
no code implementations • 28 Feb 2024 • Jiahao Huang, Liutao Yang, Fanwen Wang, Yinzhe Wu, Yang Nan, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb, Daoqiang Zhang, Guang Yang
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks.
no code implementations • 5 Feb 2024 • Andrey Bryutkin, Jiahao Huang, Zhongying Deng, Guang Yang, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks.
no code implementations • 1 Feb 2024 • Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced performance.
no code implementations • 20 Jan 2024 • Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Schönlieb
Graph Neural Networks (GNNs) have demonstrated remarkable success in modeling complex relationships in graph-structured data.
1 code implementation • 19 Dec 2023 • Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace.
no code implementations • 30 Nov 2023 • Lihao Liu, Yanqi Cheng, Zhongying Deng, Shujun Wang, Dongdong Chen, Xiaowei Hu, Pietro Liò, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero
Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.
no code implementations • 27 Nov 2023 • Teo Deveney, Jan Stanczuk, Lisa Maria Kreusser, Chris Budd, Carola-Bibiane Schönlieb
In this paper we rigorously describe the range of dynamics and approximations that arise when training score-based diffusion models, including the true SDE dynamics, the neural approximations, the various approximate particle dynamics that result, as well as their associated Fokker--Planck equations and the neural network approximations of these Fokker--Planck equations.
no code implementations • 22 Nov 2023 • Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
no code implementations • 21 Nov 2023 • Zhenda Shen, Yanqi Cheng, Raymond H. Chan, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation.
no code implementations • 16 Nov 2023 • Lihao Liu, Yanqi Cheng, Dongdong Chen, Jing He, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
In this work, we propose two innovative methods to exploit the motion prior and boost the performance of both fully-supervised and semi-supervised traffic video object detection.
no code implementations • 12 Nov 2023 • Moshe Eliasof, Davide Murari, Ferdia Sherry, Carola-Bibiane Schönlieb
Graph Neural Networks (GNNs) have established themselves as a key component in addressing diverse graph-based tasks.
no code implementations • 9 Oct 2023 • Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data.
no code implementations • 4 Oct 2023 • Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, BloodCounts! Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas Gleadall, Michael Roberts
We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.
1 code implementation • 15 Aug 2023 • Willem Diepeveen, Carlos Esteve-Yagüe, Jan Lellmann, Ozan Öktem, Carola-Bibiane Schönlieb
First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape.
no code implementations • 2 Aug 2023 • Yijun Yang, Shujun Wang, Lihao Liu, Sarah Hickman, Fiona J Gilbert, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data.
1 code implementation • 25 Jul 2023 • Sören Dittmer, Michael Roberts, Jacobus Preller, AIX COVNET, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb
We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning.
no code implementations • 18 Jul 2023 • Andreas Hauptmann, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Ferdia Sherry
While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i. e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser.
no code implementations • 14 Jul 2023 • Chaoyu Liu, Zhonghua Qiao, Chao Li, Carola-Bibiane Schönlieb
These layers can achieve specific regularization objectives and endow neural networks' outputs with corresponding properties of the evolution models.
1 code implementation • 29 Jun 2023 • Ferdia Sherry, Elena Celledoni, Matthias J. Ehrhardt, Davide Murari, Brynjulf Owren, Carola-Bibiane Schönlieb
Motivated by classical work on the numerical integration of ordinary differential equations we present a ResNet-styled neural network architecture that encodes non-expansive (1-Lipschitz) operators, as long as the spectral norms of the weights are appropriately constrained.
1 code implementation • 25 Jun 2023 • Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb, Guang Yang
Different from conventional diffusion models, the degradation operation of our CDiffMR is based on \textit{k}-space undersampling instead of adding Gaussian noise, and the restoration network is trained to harness a de-aliaseing function.
1 code implementation • 15 Jun 2023 • Daniel Kreuter, Samuel Tull, Julian Gilbey, Jacobus Preller, BloodCounts! Consortium, John A. D. Aston, James H. F. Rudd, Suthesh Sivapalaratnam, Carola-Bibiane Schönlieb, Nicholas Gleadall, Michael Roberts
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites.
no code implementations • 24 Apr 2023 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb
This issue stems from the unrealistic assumption that approximates the conditional data distribution, $p(\textbf{x} | \textbf{z})$, as an isotropic Gaussian.
1 code implementation • 19 Mar 2023 • Yijun Yang, Huazhu Fu, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb, Lei Zhu
However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification.
no code implementations • 18 Mar 2023 • Shujun Wang, Angelica I Aviles-Rivero, Zoe Kourtzi, Carola-Bibiane Schönlieb
We demonstrate, through extensive experiments on ADNI, that our proposed HGIB framework outperforms existing state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis.
1 code implementation • 15 Mar 2023 • Jan Oscar Cross-Zamirski, Praveen Anand, Guy Williams, Elizabeth Mouchet, Yinhai Wang, Carola-Bibiane Schönlieb
Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains.
no code implementations • 14 Mar 2023 • Jing Zou, Noémie Debroux, Lihao Liu, Jing Qin, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
We propose a novel framework for deformable image registration.
1 code implementation • 11 Mar 2023 • Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
1 code implementation • 9 Mar 2023 • Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb
Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inverse problems and imaging.
1 code implementation • 7 Mar 2023 • Tamara G. Grossmann, Carola-Bibiane Schönlieb, Orietta Da Rold
This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet.
no code implementations • 24 Feb 2023 • Simone Saitta, Marcello Carioni, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Alberto Redaelli
4D flow MRI is a non-invasive imaging method that can measure blood flow velocities over time.
1 code implementation • 8 Feb 2023 • Tamara G. Grossmann, Urszula Julia Komorowska, Jonas Latz, Carola-Bibiane Schönlieb
In terms of solution time and accuracy, physics-informed neural networks have not been able to outperform the finite element method in our study.
no code implementations • 1 Feb 2023 • Chun-Wun Cheng, Christina Runkel, Lihao Liu, Raymond H Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations.
no code implementations • 23 Dec 2022 • Jan Stanczuk, Georgios Batzolis, Teo Deveney, Carola-Bibiane Schönlieb
A diffusion model approximates the score function i. e. the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption.
no code implementations • CVPR 2023 • Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
no code implementations • 21 Oct 2022 • Sören Dittmer, Michael Roberts, Julian Gilbey, Ander Biguri, AIX-COVNET Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.
1 code implementation • 5 Oct 2022 • Elena Celledoni, Davide Murari, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
The structure of the neural network is then inferred from the properties of the ODE vector field.
no code implementations • 18 Sep 2022 • Yanqi Cheng, Lihao Liu, Shujun Wang, Yueming Jin, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
This is the question that we address in this work.
1 code implementation • 16 Sep 2022 • Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet, Carola-Bibiane Schönlieb, Riku Turkki, Yinhai Wang
We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells.
1 code implementation • 12 Sep 2022 • Carlos Esteve-Yagüe, Willem Diepeveen, Ozan Öktem, Carola-Bibiane Schönlieb
The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, taken from different viewing directions, and in the heterogeneous case, each image corresponds to a different conformation of the macromolecule.
no code implementations • 5 Sep 2022 • Dongdong Chen, Mike Davies, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb, Ferdia Sherry, Julián Tachella
From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry.
no code implementations • 31 Aug 2022 • Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb
In this work we propose a new paradigm for designing efficient deep unrolling networks using dimensionality reduction schemes, including minibatch gradient approximation and operator sketching.
no code implementations • 18 Aug 2022 • Debmita Bandyopadhyay, Subhadip Mukherjee, James Ball, Grégoire Vincent, David A. Coomes, Carola-Bibiane Schönlieb
We propose a novel graph-regularized neural network (GRNN) algorithm for tree species classification.
no code implementations • 11 Aug 2022 • Jeremy Budd, Yves van Gennip, Jonas Latz, Simone Parisotto, Carola-Bibiane Schönlieb
Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations.
no code implementations • 2 Aug 2022 • Junqi Tang, Matthias Ehrhardt, Carola-Bibiane Schönlieb
In this work we propose a stochastic primal-dual preconditioned three-operator splitting algorithm for solving a class of convex three-composite optimization problems.
no code implementations • 20 Jul 2022 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows.
no code implementations • 16 Jun 2022 • Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro Lio, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
no code implementations • 11 Jun 2022 • Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, Carola-Bibiane Schönlieb
In recent years, deep learning has achieved remarkable empirical success for image reconstruction.
no code implementations • 9 Jun 2022 • Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb
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.
no code implementations • 4 Apr 2022 • Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Schönlieb
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
no code implementations • 21 Mar 2022 • Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb
In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching.
no code implementations • 21 Mar 2022 • Yiran Wei, Xi Chen, Lei Zhu, Lipei Zhang, Carola-Bibiane Schönlieb, Stephen J. Price, Chao Li
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.
no code implementations • 14 Mar 2022 • Simone Parisotto, Ninetta Leone, Carola-Bibiane Schönlieb, Alessandro Launaro
In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds.
no code implementations • 10 Mar 2022 • Lihao Liu, Zhening Huang, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.
no code implementations • 8 Mar 2022 • Lipei Zhang, Yiran Wei, Ying Fu, Stephen Price, Carola-Bibiane Schönlieb, Chao Li
In this proposed scheme, we design a normalized modality contrastive loss (NMC-loss), which could promote to disentangle multi-modality complementary representation of FFPE and frozen sections from the same patient.
no code implementations • 8 Mar 2022 • Yiran Wei, Stephen J. Price, Carola-Bibiane Schönlieb, Chao Li
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.
1 code implementation • 1 Mar 2022 • Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework.
no code implementations • 14 Jan 2022 • Yiran Wei, Chao Li, Xi Chen, Carola-Bibiane Schönlieb, Stephen J. Price
Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone.
no code implementations • 23 Dec 2021 • Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò, Tim Leiner, Pier Luigi Dragotti, Ge Wang, Daniel Rueckert, David Firmin, Guang Yang
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.
no code implementations • 7 Dec 2021 • Kexin Jin, Jonas Latz, ChenGuang Liu, Carola-Bibiane Schönlieb
Optimization problems with continuous data appear in, e. g., robust machine learning, functional data analysis, and variational inference.
1 code implementation • 26 Nov 2021 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
1 code implementation • 31 Oct 2021 • Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang
However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice.
no code implementations • 31 Oct 2021 • Michael Yeung, Guang Yang, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo
Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.
no code implementations • 31 Oct 2021 • Michael Yeung, Leonardo Rundo, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger
Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Jevgenija Rudzusika, Buda Bajic, Ozan Öktem, Carola-Bibiane Schönlieb, Christian Etmann
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction.
no code implementations • 19 Oct 2021 • Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb
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.
no code implementations • 7 Oct 2021 • Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb
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.
no code implementations • 4 Sep 2021 • Yiran Wei, Yonghao Li, Xi Chen, Carola-Bibiane Schönlieb, Chao Li, Stephen J. Price
Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients.
no code implementations • 21 Aug 2021 • YiFan Li, Chao Li, Yiran Wei, Stephen Price, Carola-Bibiane Schönlieb, Xi Chen
In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.
1 code implementation • 8 Jun 2021 • Philip Sellars, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
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.
1 code implementation • 7 Jun 2021 • Hankui Peng, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously.
1 code implementation • NeurIPS 2021 • Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems.
no code implementations • 16 May 2021 • Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo
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.
no code implementations • 10 May 2021 • Julian D. Gilbey, Carola-Bibiane Schönlieb
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.
Optical Character Recognition Optical Character Recognition (OCR) +1
no code implementations • 27 Apr 2021 • Madeleine Kotzagiannidis, Carola-Bibiane Schönlieb
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.
1 code implementation • 30 Mar 2021 • Subhadip Mukherjee, Ozan Öktem, Carola-Bibiane Schönlieb
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.
no code implementations • 2 Mar 2021 • Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.
1 code implementation • 23 Feb 2021 • Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
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.
no code implementations • 12 Feb 2021 • Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network.
5 code implementations • 8 Feb 2021 • Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo
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.
no code implementations • 21 Jan 2021 • Chao Li, Wenjian Huang, Xi Chen, Yiran Wei, Stephen J. Price, Carola-Bibiane Schönlieb
EMReDL showed to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration.
no code implementations • 20 Jan 2021 • Tao Wei, Angelica I Aviles-Rivero, Shuo Wang, Yuan Huang, Fiona J Gilbert, Carola-Bibiane Schönlieb, Chang Wen Chen
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Cancer-no cancer per image classification Image Classification +3
no code implementations • 5 Dec 2020 • YiFan Li, Chao Li, Stephen Price, Carola-Bibiane Schönlieb, Xi Chen
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.
1 code implementation • 1 Dec 2020 • Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey, Saikumar Reddy, Carola-Bibiane Schönlieb
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.
1 code implementation • 18 Nov 2020 • Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb
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.
1 code implementation • 17 Nov 2020 • Lihao Liu, Angelica I Aviles-Rivero, Carola-Bibiane Schönlieb
Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.
no code implementations • 8 Oct 2020 • Veronica Corona, Yehuda Dar, Guy Williams, Carola-Bibiane Schönlieb
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.
no code implementations • 30 Sep 2020 • Angelica I. Aviles-Rivero, Philip Sellars, Carola-Bibiane Schönlieb, Nicolas Papadakis
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
no code implementations • 23 Sep 2020 • Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis
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.
1 code implementation • 14 Aug 2020 • Rihuan Ke, Carola-Bibiane Schönlieb
The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications.
no code implementations • 14 Aug 2020 • Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb
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.
1 code implementation • 6 Aug 2020 • Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
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.
1 code implementation • 3 Jul 2020 • Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals.
1 code implementation • NeurIPS 2020 • Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb
To the best of our knowledge, this is the first approach towards learning a non-linear spectral decomposition of images.
2 code implementations • 11 Jun 2020 • Jonathan Williams, Carola-Bibiane Schönlieb, Tom Swinfield, Bambang Irawan, Eva Achmad, Muhammad Zudhi, Habibi, Elva Gemita, David A. Coomes
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.
no code implementations • 5 Jun 2020 • Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Robert I McLachlan, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
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.
no code implementations • 4 Jun 2020 • Simone Parisotto, Alessandro Launaro, Ninetta Leone, Carola-Bibiane Schönlieb
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.
no code implementations • 26 May 2020 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Mark Kirkland, Peter Schuetz, Carola-Bibiane Schönlieb
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.
no code implementations • 15 May 2020 • Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek
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.
1 code implementation • 14 May 2020 • Sebastian Lunz, Andreas Hauptmann, Tanja Tarvainen, Carola-Bibiane Schönlieb, Simon Arridge
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.
2 code implementations • 11 May 2020 • Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.
no code implementations • 27 Feb 2020 • Derek Driggs, Junqi Tang, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb
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
1 code implementation • ICML 2020 • Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
no code implementations • 16 Dec 2019 • Jiulong Liu, Angelica I. Aviles-Rivero, Hui Ji, Carola-Bibiane Schönlieb
We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently.
1 code implementation • 5 Dec 2019 • Martin Burger, Yury Korolev, Simone Parisotto, Carola-Bibiane Schönlieb
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
no code implementations • 10 Oct 2019 • Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb
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.
no code implementations • 4 Oct 2019 • Simone Parisotto, Luca Calatroni, Aurélie Bugeau, Nicolas Papadakis, Carola-Bibiane Schönlieb
We propose a new variational model for non-linear image fusion.
no code implementations • 25 Sep 2019 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
no code implementations • 23 Sep 2019 • Oliver M. Crook, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis
In this paper we extend the labels by minimising the constrained discrete $p$-Dirichlet energy.
no code implementations • 23 Jul 2019 • Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Qingnan Fan, Robby T. Tan, Carola-Bibiane Schönlieb
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
no code implementations • 20 Jun 2019 • Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Samar M Alsaleh, Robby T Tan, Carola-Bibiane Schönlieb
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.
2 code implementations • 20 Jun 2019 • Ferdia Sherry, Martin Benning, Juan Carlos De los Reyes, Martin J. Graves, Georg Maierhofer, Guy Williams, Carola-Bibiane Schönlieb, Matthias J. Ehrhardt
The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete".
no code implementations • 20 Jun 2019 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
1 code implementation • 10 May 2019 • Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb
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.
no code implementations • 11 Apr 2019 • Martin Benning, Elena Celledoni, Matthias J. Ehrhardt, Brynjulf Owren, Carola-Bibiane Schönlieb
We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation.
1 code implementation • 20 Mar 2019 • Jonathan Williams, Carola-Bibiane Schönlieb, Tom Swinfield, Juheon Lee, Xiaohao Cai, Lan Qie, David A. Coomes
From these three-dimensional crowns, we are able to measure individual tree biomass.
1 code implementation • 14 Mar 2019 • Philip Sellars, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.
no code implementations • 12 Mar 2019 • Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb, Christiane Stollenwerk
We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially).
1 code implementation • 12 Dec 2018 • Simone Parisotto, Jan Lellmann, Simon Masnou, Carola-Bibiane Schönlieb
We introduce a new class of higher-order total directional variation regularizers.
Numerical Analysis 47A52, 49M30, 49N45, 65J22, 94A08
1 code implementation • 17 Sep 2018 • Simone Parisotto, Luca Calatroni, Marco Caliari, Carola-Bibiane Schönlieb, Joachim Weickert
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,
no code implementations • 27 Aug 2018 • Jonas Adler, Sebastian Lunz, Olivier Verdier, Carola-Bibiane Schönlieb, Ozan Öktem
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.
1 code implementation • 21 Aug 2018 • Matthias J. Ehrhardt, Pawel Markiewicz, Carola-Bibiane Schönlieb
Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins).
2 code implementations • NeurIPS 2018 • Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods.
no code implementations • 29 May 2018 • Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero, Qingnan Fan, Dong-Dong Chen, Carola-Bibiane Schönlieb, Sabine Süsstrunk
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.
1 code implementation • 19 Mar 2018 • Luca Calatroni, Marie d'Autume, Rob Hocking, Stella Panayotova, Simone Parisotto, Paola Ricciardi, Carola-Bibiane Schönlieb
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.
no code implementations • 8 Feb 2018 • Georg Maierhofer, Daniel Heydecker, Angelica I. Aviles-Rivero, Samar M. Alsaleh, Carola-Bibiane Schönlieb
This paper addresses the search for a fast and meaningful image segmentation in the context of $k$-means clustering.
2 code implementations • 4 Oct 2017 • Leon Bungert, David A. Coomes, Matthias J. Ehrhardt, Jennifer Rasch, Rafael Reisenhofer, Carola-Bibiane Schönlieb
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.
2 code implementations • 15 Jun 2017 • Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb
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.
no code implementations • 23 Mar 2017 • Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb
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.
no code implementations • 12 Jul 2016 • Martin Burger, Hendrik Dirks, Carola-Bibiane Schönlieb
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.
no code implementations • 27 Feb 2016 • Luca Calatroni, Yves van Gennip, Carola-Bibiane Schönlieb, Hannah Rowland, Arjuna Flenner
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).
1 code implementation • 8 May 2015 • Luca Calatroni, Cao Chung, Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen
We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space.
no code implementations • 8 May 2015 • Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen
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.