469 citations · 1.6k across the 93 of their papers we have counts for
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Active Sampling for MRI-based Sequential Decision Making
Yuning Du, Jingshuai Liu, Rohan Dharmakumar +1
Despite the superior diagnostic capability of Magnetic Resonance Imaging (MRI), its use as a Point-of-Care (PoC) device remains limited by high cost and complexity. To enable such…
CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning
Gianluca Carloni, Sotirios A Tsaftaris, Sara Colantonio
Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one. For instance, a classifier trained on chest X-ray (CXR…
Erase to Enhance: Data-Efficient Machine Unlearning in MRI Reconstruction
Yuyang Xue, Jingshuai Liu, Steven McDonagh +1
Machine unlearning is a promising paradigm for removing unwanted data samples from a trained model, towards ensuring compliance with privacy regulations and limiting harmful biases…
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…
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-…
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Yuyang Xue, Yuning Du, Gianluca Carloni +3
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the -space is employed to reduce…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
The role of noise in denoising models for anomaly detection in medical images
Antanas Kascenas, Pedro Sanchez, Patrick Schrempf +9
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are dif…
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification
Tian Xia, Pedro Sanchez, Chen Qin +1
Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
Thanks to their ability to learn data distributions without requiring paired data, Generative Adversarial Networks (GANs) have become an integral part of many computer vision metho…
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +4
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient c…
Controllable cardiac synthesis via disentangled anatomy arithmetic
Spyridon Thermos, Xiao Liu, Alison O'Neil +1
Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias +1
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the…
Semi-supervised Pathology Segmentation with Disentangled Representations
Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang +6
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining…
Disentangled Representations for Domain-generalized Cardiac Segmentation
Xiao Liu, Spyridon Thermos, Agisilaos Chartsias +2
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. S…
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and under…
Learning to synthesise the ageing brain without longitudinal data
Tian Xia, Agisilaos Chartsias, Chengjia Wang +1
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data)…
Conditioning Convolutional Segmentation Architectures with Non-Imaging Data
Grzegorz Jacenków, Agisilaos Chartsias, Brian Mohr +1
We compare two conditioning mechanisms based on concatenation and feature-wise modulation to integrate non-imaging information into convolutional neural networks for segmentation o…