1.4k citations · 3k across the 54 of their papers we have counts for
13 papers · 1 filter
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
Jingqing Zhang, Luis Bolanos, Tong Li +6
Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remai…
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…
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
Eloise Withnell, Xiaoyu Zhang, Kai Sun +1
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the p…
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
César Quilodrán-Casas, Rossella Arcucci, Laetitia Mottet +2
This paper presents an approach to improve the forecast of computational fluid dynamics (CFD) simulations of urban air pollution using deep learning, and most specifically adversar…
MOAI: A methodology for evaluating the impact of indoor airflow in the transmission of COVID-19
Axel Oehmichen, Florian Guitton, Cedric Wahl +3
Epidemiology models play a key role in understanding and responding to the COVID-19 pandemic. In order to build those models, scientists need to understand contributing factors and…
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…
Verifying Design through Generative Visualization of Neural Activities
Pan Wang, Danlin Peng, Simiao Yu +4
Current neuroscience focused approaches for evaluating the effectiveness of a design do not use direct visualisation of mental activity. A recurrent neural network is used as the e…
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,…
OmiEmbed: a unified multi-task deep learning framework for multi-omics data
Xiaoyu Zhang, Yuting Xing, Kai Sun +1
High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wid…
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
César Quilodrán-Casas, Vinicius Santos Silva, Rossella Arcucci +3
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus…
A Blockchain-based Trust System for Decentralised Applications: When trustless needs trust
Nguyen Truong, Gyu Myoung Lee, Kai Sun +2
Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary. Such DApps open a market…
BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing
Zhe Peng, Jinbin Huang, Haixin Wang +8
The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has show…
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
César Quilodrán-Casas, Rossella Arcucci, Christopher Pain +1
This paper presents an approach to improve computational fluid dynamics simulations forecasts of air pollution using deep learning. Our method, which integrates Principal Component…