Search Results for author: David A. Clifton

Found 36 papers, 16 papers with code

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study

no code implementations18 Jun 2023 Rushuang Zhou, Lei Lu, Zijun Liu, Ting Xiang, Zhen Liang, David A. Clifton, Yining Dong, Yuan-Ting Zhang

However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.

Data Augmentation Electrocardiography (ECG) +2

A Brief Review of Hypernetworks in Deep Learning

no code implementations12 Jun 2023 Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu, Soheila Molaei, David A. Clifton

They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks.

Causal Inference Continual Learning +3

Dynamic Inter-treatment Information Sharing for Heterogeneous Treatment Effects Estimation

no code implementations25 May 2023 Vinod Kumar Chauhan, Jiandong Zhou, Soheila Molaei, Ghadeer Ghosheh, David A. Clifton

Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets.

Counterfactual Inference

Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention

1 code implementation5 May 2023 Anshul Thakur, Tingting Zhu, Vinayak Abrol, Jacob Armstrong, Yujiang Wang, David A. Clifton

Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.

Time Series

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation

1 code implementation11 Mar 2023 Bang Yang, Fenglin Liu, Yuexian Zou, Xian Wu, YaoWei Wang, David A. Clifton

We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and believable outputs and significantly outperforms existing zero-shot methods.

Image Captioning Machine Translation +5

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

2 code implementations3 Feb 2023 Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S Kevin Zhou, Lawrence Hamilton Staib, James S Duncan

For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples.

Contrastive Learning Image Segmentation +3

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

4 code implementations21 Nov 2022 Peng Jin, Jinfa Huang, Fenglin Liu, Xian Wu, Shen Ge, Guoli Song, David A. Clifton, Jie Chen

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 the semantic similarities of text-video pairs.

Ranked #2 on Video Retrieval on LSMDC (text-to-video Mean Rank metric)

Contrastive Learning Representation Learning +5

Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model

2 code implementations23 Oct 2022 Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Zhangdaihong Liu, Xu sun, Yang Yang, David A. Clifton

We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.

Adversarial De-confounding in Individualised Treatment Effects Estimation

no code implementations19 Oct 2022 Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc.

Counterfactual Inference

MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers

1 code implementation12 Oct 2022 Mohammadmahdi Nouriborji, Omid Rohanian, Samaneh Kouchaki, David A. Clifton

Different strategies have been proposed in the literature to alleviate these problems, with the aim to create effective compact models that nearly match the performance of their bloated counterparts with negligible performance losses.

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 Sep 2022 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan

Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.

Anatomy Contrastive Learning +3

On the Effectiveness of Compact Biomedical Transformers

1 code implementation7 Sep 2022 Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki, David A. Clifton

Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks.

Continual Learning Knowledge Distillation +1

COPER: Continuous Patient State Perceiver

1 code implementation5 Aug 2022 Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue, David A. Clifton

COPER uses Perceiver model and the concept of neural ordinary differential equations (ODEs) to learn the continuous time dynamics of patient state, i. e., continuity of input space and continuity of output space.

Irregular Time Series Mortality Prediction +2

Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

1 code implementation24 Jul 2022 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.

Time Series Time Series Analysis

Multimodal Learning with Transformers: A Survey

no code implementations13 Jun 2022 Peng Xu, Xiatian Zhu, David A. Clifton

Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks.

How to Understand Masked Autoencoders

no code implementations8 Feb 2022 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, but is also a milestone that bridges the gap between visual and linguistic masked autoencoding (BERT-style) pre-trainings.

Self-Supervised Learning

Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning

no code implementations4 Jul 2021 Rasheed el-Bouri, Tingting Zhu, David A. Clifton

In this work, we aim to utilise patient data extracted from multiple hospital data centres to train a machine learning model without sacrificing patient privacy.

Federated Learning Scheduling +1

PCPs: Patient Cardiac Prototypes

no code implementations28 Nov 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

Many clinical deep learning algorithms are population-based and difficult to interpret.

Contrastive Learning

CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age

no code implementations NeurIPS 2021 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.

Clustering Contrastive Learning +2

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

1 code implementation27 May 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another.

Contrastive Learning

ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks

5 code implementations CVPR 2021 Xinshao Wang, Yang Hua, Elyor Kodirov, David A. Clifton, Neil M. Robertson

Keywords: entropy minimisation, maximum entropy, confidence penalty, self knowledge distillation, label correction, label noise, semi-supervised learning, output regularisation

Self-Knowledge Distillation

SoQal: Selective Oracle Questioning in Active Learning

no code implementations22 Apr 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

Large sets of unlabelled data within the healthcare domain remain underutilized.

Active Learning

CLOPS: Continual Learning of Physiological Signals

no code implementations20 Apr 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i. i. d).

Continual Learning Multi-Task Learning

SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

2 code implementations20 Apr 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.

Active Learning Pseudo Label +1

Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality

no code implementations1 Dec 2019 Pulkit Sharma, Farah E. Shamout, David A. Clifton

Machine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality.

Federated Learning

Fusing Continuous-valued Medical Labels using a Bayesian Model

no code implementations23 Mar 2015 Tingting Zhu, Nic Dunkley, Joachim Behar, David A. Clifton, Gari. D. Clifford

To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm.

Time Series Time Series Analysis

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