41 citations · 55 across the 15 of their papers we have counts for
15 papers
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We…
Inference Stage Denoising for Undersampled MRI Reconstruction
Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris
Reconstruction of magnetic resonance imaging (MRI) data has been positively affected by deep learning. A key challenge remains: to improve generalisation to distribution shifts bet…
Group Distributionally Robust Knowledge Distillation
Konstantinos Vilouras, Xiao Liu, Pedro Sanchez +2
Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one. However, distillation objectives are susceptible to sub-populat…
Compositional Representation Learning for Brain Tumour Segmentation
Xiao Liu, Antanas Kascenas, Hannah Watson +2
For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations. However, the expensive exer…
Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction
Yuning Du, Yuyang Xue, Rohan Dharmakumar +1
Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-…
Debiasing Counterfactuals In the Presence of Spurious Correlations
Amar Kumar, Nima Fathi, Raghav Mehta +4
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i.e. confounders), should they b…