297 citations · 1.5k across the 199 of their papers we have counts for
18 papers · 1 filter
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò +6
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep n…
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Kexin Jin, Jonas Latz, Chenguang Liu +1
Optimization problems with continuous data appear in, e.g., robust machine learning, functional data analysis, and variational inference. Here, the target function is given as an i…
Conditional Image Generation with Score-Based Diffusion Models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb +1
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling. In this work we conduct a systematic comparison and theoretical ana…
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Xiang Bai, Hanchen Wang, Liya Ma +43
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection…
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
Michael Yeung, Leonardo Rundo, Yang Nan +3
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well…
Learning convex regularizers satisfying the variational source condition for inverse problems
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. With the emergence and astonishing…
Stochastic Primal-Dual Deep Unrolling
Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb
We propose a new type of efficient deep-unrolling networks for solving imaging inverse problems. Conventional deep-unrolling methods require full forward operator and its adjoint a…
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
Yifan Li, Chao Li, Yiran Wei +3
Glioblastoma is profoundly heterogeneous in regional microstructure and vasculature. Characterizing the spatial heterogeneity of glioblastoma could lead to more precise treatment.…
End-to-end reconstruction meets data-driven regularization for inverse problems
Subhadip Mukherjee, Marcello Carioni, Ozan Öktem +1
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems. The proposed method combines the classical variational framewor…
CAFLOW: Conditional Autoregressive Flows
Georgios Batzolis, Marcello Carioni, Christian Etmann +3
We introduce CAFLOW, a new diverse image-to-image translation model that simultaneously leverages the power of auto-regressive modeling and the modeling efficiency of conditional n…
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
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…
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Hankui Peng, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
Superpixels serve as a powerful preprocessing tool in numerous computer vision tasks. By using superpixel representation, the number of image primitives can be largely reduced by o…
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images
Julian D. Gilbey, Carola-Bibiane Schönlieb
Some historical and more recent printed documents have been scanned or stored at very low resolutions, such as 60 dpi. Though such scans are relatively easy for humans to read, the…
Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data
Madeleine Kotzagiannidis, Carola-Bibiane Schönlieb
Graphs naturally lend themselves to model the complexities of Hyperspectral Image (HSI) data as well as to serve as semi-supervised classifiers by propagating given labels among ne…
Adversarially learned iterative reconstruction for imaging inverse problems
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 l…
Efficient Global Optimization of Non-differentiable, Symmetric Objectives for Multi Camera Placement
Maria L. Hänel, Carola-B. Schönlieb
We propose a novel iterative method for optimally placing and orienting multiple cameras in a 3D scene. Sample applications include improving the accuracy of 3D reconstruction, max…
Equivariant neural networks for inverse problems
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann +3
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of…
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb +1
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art…