Text-to-image generation (TTI) refers to the usage of models that could
...
Electrocardiography (ECG) is a non-invasive tool for predicting
cardiova...
Hypernetworks, or hypernets in short, are neural networks that generate
...
Existing heterogeneous treatment effects learners, also known as conditi...
The lack of data democratization and information leakage from trained mo...
Natural Language Generation (NLG) accepts input data in the form of imag...
Electronic Health Records (EHRs) contain sensitive patient information, ...
Specialised pre-trained language models are becoming more frequent in NL...
For medical image segmentation, contrastive learning is the dominant pra...
Most video-and-language representation learning approaches employ contra...
The "Patient Instruction" (PI), which contains critical instructional
in...
Pre-trained Language Models (LMs) have become an integral part of Natura...
Language models pre-trained on biomedical corpora, such as BioBERT, have...
In electronic health records (EHRs), irregular time-series (ITS) occur
n...
A particular challenge for disease progression modeling is the heterogen...
To train robust deep neural networks (DNNs), we systematically study sev...
Transformer is a promising neural network learner, and has achieved grea...
Advances in deep learning for human activity recognition have been relat...
With the rapid growth of memory and computing power, datasets are becomi...
"Masked Autoencoders (MAE) Are Scalable Vision Learners" revolutionizes ...
Given the abundance and ease of access of personal data today, individua...
Mutual knowledge distillation (MKD) improves a model by distilling knowl...
The ongoing digitization of health records within the healthcare industr...
Many clinical deep learning algorithms are population-based and difficul...
The healthcare industry generates troves of unlabelled physiological dat...
Large sets of unlabelled data within the healthcare domain remain
underu...
Deep learning algorithms are known to experience destructive interferenc...
Small, labelled datasets in the presence of larger, unlabelled datasets ...
Hand, foot and mouth disease (HFMD) and tetanus are serious infectious
d...
Machine learning models can be used for pattern recognition in medical d...