1.4k citations · 3k across the 54 of their papers we have counts for
55 papers
Honest Computing: Achieving demonstrable data lineage and provenance for driving data and process-sensitive policies
Florian Guitton, Axel Oehmichen, Étienne Bossé +1
Data is the foundation of any scientific, industrial or commercial process. Its journey typically flows from collection to transport, storage, management and processing. While best…
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training
Jiuming Qin, Che Liu, Sibo Cheng +2
Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large…
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency
Che Liu, Cheng Ouyang, Yinda Chen +7
While 3D visual self-supervised learning (vSSL) shows promising results in capturing visual representations, it overlooks the clinical knowledge from radiology reports. Meanwhile,…
Efficient deep data assimilation with sparse observations and time-varying sensors
Sibo Cheng, Che Liu, Yike Guo +1
Variational Data Assimilation (DA) has been broadly used in engineering problems for field reconstruction and prediction by performing a weighted combination of multiple sources of…
Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects
Chenhong Zhou, Xiaorui Zhang, Meng Gao +3
Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere's abilit…
Analyzing drop coalescence in microfluidic device with a deep learning generative model
Kewei Zhu, Sibo Cheng, Nina Kovalchuk +4
Predicting drop coalescence based on process parameters is crucial for experiment design in chemical engineering. However, predictive models can suffer from the lack of training da…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge
Ashwani Tanwar, Jingqing Zhang, Julia Ive +2
Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases. However, reasoning with nu…
Label-dependent and event-guided interpretable disease risk prediction using EHRs
Shuai Niu, Yunya Song, Qing Yin +2
Electronic health records (EHRs) contain patients' heterogeneous data that are collected from medical providers involved in the patient's care, including medical notes, clinical ev…
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records
Shuai Niu, Qing Yin, Yunya Song +2
Disease risk prediction has attracted increasing attention in the field of modern healthcare, especially with the latest advances in artificial intelligence (AI). Electronic health…
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…
Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective
Nguyen Truong, Kai Sun, Siyao Wang +2
Along with the blooming of AI and Machine Learning-based applications and services, data privacy and security have become a critical challenge. Conventionally, data is collected an…