297 citations · 1.5k across the 199 of their papers we have counts for
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TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang +3
Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal algorithms, for example, the alternating direction method of multipliers (ADMM), with advanced den…
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
Yifan Li, Chao Li, Stephen Price +2
Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor…
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey +2
Semantic segmentation has been widely investigated in the community, in which the state of the art techniques are based on supervised models. Those models have reported unprecedent…
Contrastive Registration for Unsupervised Medical Image Segmentation
Lihao Liu, Angelica I Aviles-Rivero, Carola-Bibiane Schönlieb
Medical image segmentation is a relevant task as it serves as the first step for several diagnosis processes, thus it is indispensable in clinical usage. Whilst major success has b…
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
Angelica I Aviles-Rivero, Philip Sellars, Carola-Bibiane Schönlieb +1
Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing Artificial Intelligence techniqu…
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov +3
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.…
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
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. For large-scale data, as it for e…
Learning Optical Flow for Fast MRI Reconstruction
T. Schmoderer, A. I Aviles-Rivero, V. Corona +2
Reconstructing high-quality magnetic resonance images (MRI) from undersampled raw data is of great interest from both technical and clinical point of views. To this date, however,…
SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization
Derek Driggs, Junqi Tang, Jingwei Liang +2
We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems. Large-scale i…
CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification
Philip Sellars, Angelica Aviles-Rivero, Carola Bibiane Schönlieb
Given the potential difficulties in obtaining large quantities of labelled data, many works have explored the use of deep semi-supervised learning, which uses both labelled and unl…