58 citations · 204 across the 20 of their papers we have counts for
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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation
Chen Chen, Chen Qin, Cheng Ouyang +7
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large m…
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
Nicolo Savioli, Antonio de Marvao, Wenjia Bai +5
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion. However, techniques such as cardiac magnetic resonance imaging a…
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
Shuo Wang, Chen Qin, Nicolo Savioli +6
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the…
Product semantics translation from brain activity via adversarial learning
Pan Wang, Zhifeng Gong, Shuo Wang +5
A small change of design semantics may affect a user's satisfaction with a product. To modify a design semantic of a given product from personalised brain activity via adversarial…
A General Framework for Revealing Human Mind with auto-encoding GANs
Pan Wang, Rui Zhou, Shuo Wang +6
Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image,…