144 citations · 611 across the 31 of their papers we have counts for
32 papers
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study
Rushuang Zhou, Lei Lu, Zijun Liu +5
Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid de…
A Brief Review of Hypernetworks in Deep Learning
Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu +2
Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learn…
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation
Bang Yang, Fenglin Liu, Yuexian Zou +3
Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly…
Lightweight Transformers for Clinical Natural Language Processing
Omid Rohanian, Mohammadmahdi Nouriborji, Hannah Jauncey +5
Specialised pre-trained language models are becoming more frequent in NLP since they can potentially outperform models trained on generic texts. BioBERT and BioClinicalBERT are two…
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min +5
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pa…
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Peng Jin, Jinfa Huang, Fenglin Liu +5
Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to t…
Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine
Fenglin Liu, Bang Yang, Chenyu You +6
Language models (LMs), including large language models (such as ChatGPT), have the potential to assist clinicians in generating various clinical notes. However, LMs are prone to pr…
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Mohammadmahdi Nouriborji, Omid Rohanian, Samaneh Kouchaki +1
Pre-trained Language Models (LMs) have become an integral part of Natural Language Processing (NLP) in recent years, due to their superior performance in downstream applications. I…
On the Effectiveness of Compact Biomedical Transformers
Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki +1
Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the o…
COPER: Continuous Patient State Perceiver
Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue +1
In electronic health records (EHRs), irregular time-series (ITS) occur naturally due to patient health dynamics, reflected by irregular hospital visits, diseases/conditions and the…
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
Taha Ceritli, Andrew P. Creagh, David A. Clifton
A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a…
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Xinshao Wang, Yang Hua, Elyor Kodirov +3
There is a family of label modification approaches including self and non-self label correction (LC), and output regularisation. They are widely used for training robust deep neura…
Multimodal Learning with Transformers: A Survey
Peng Xu, Xiatian Zhu, David A. Clifton
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and…
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Hang Yuan, Shing Chan, Andrew P. Creagh +4
Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learnin…
How to Understand Masked Autoencoders
Shuhao Cao, Peng Xu, David A. Clifton
"Masked Autoencoders (MAE) Are Scalable Vision Learners" revolutionizes the self-supervised learning method in that it not only achieves the state-of-the-art for image pre-training…
Continual learning of longitudinal health records
J. Armstrong, D. Clifton
Continual learning denotes machine learning methods which can adapt to new environments while retaining and reusing knowledge gained from past experiences. Such methods address two…
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
Rasheed el-Bouri, Tingting Zhu, David A. Clifton
Given the abundance and ease of access of personal data today, individual privacy has become of paramount importance, particularly in the healthcare domain. In this work, we aim to…
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Ziyun Li, Xinshao Wang, Di Hu +4
Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under advers…
Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David Clifton
Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the…
PCPs: Patient Cardiac Prototypes
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Many clinical deep learning algorithms are population-based and difficult to interpret. Such properties limit their clinical utility as population-based findings may not generalize…
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
Dani Kiyasseh, Tingting Zhu, David A. Clifton
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease…
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Rasheed el-Bouri, David Eyre, Peter Watkinson +2
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing wit…
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Dani Kiyasseh, Tingting Zhu, David A. Clifton
The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourage…
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Xinshao Wang, Yang Hua, Elyor Kodirov +2
To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction…