160 citations · 218 across the 13 of their papers we have counts for
13 papers
P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data
Yucheng Ruan, Xiang Lan, Daniel J. Tan +2
Medical tabular data, abundant in Electronic Health Records (EHRs), is a valuable resource for diverse medical tasks such as risk prediction. While deep learning approaches, partic…
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Xiang Lan, Hanshu Yan, Shenda Hong +1
Not all positive pairs are beneficial to time series contrastive learning. In this paper, we study two types of bad positive pairs that can impair the quality of time series repres…
FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images
Young Seok Jeon, Hongfei Yang, Mengling Feng
The encoder-decoder model is a commonly used Deep Neural Network (DNN) model for medical image segmentation. Conventional encoder-decoder models make pixel-wise predictions focusin…
UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio
Jiangeng Chang, Yucheng Ruan, Cui Shaoze +2
We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to q…
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals
Xiang Lan, Dianwen Ng, Shenda Hong +1
Learning information-rich and generalizable representations effectively from unlabeled multivariate cardiac signals to identify abnormal heart rhythms (cardiac arrhythmias) is valu…
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Feng Xie, Han Yuan, Yilin Ning +5
Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, c…
DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021
Jiangeng Chang, Shaoze Cui, Mengling Feng
In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs. Since…
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms
Hao Du, Melissa Min-Szu Yao, Liangyu Chen +2
The morphology and distribution of microcalcifications in a cluster are the most important characteristics for radiologists to diagnose breast cancer. However, it is time-consuming…
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Zhaowei Zhu, Xiang Lan, Tingting Zhao +8
Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abno…
An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning
Wang Han, Wesley Yeung, Angeline Tung +4
Clinical performance audits are routinely performed in Emergency Medical Services (EMS) to ensure adherence to treatment protocols, to identify individual areas of weakness for rem…
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis
Hao Du, Jiashi Feng, Mengling Feng
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination. Inspired…
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
Siqi Liu, Kee Yuan Ngiam, Mengling Feng
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in…
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care
Ziyuan Pan, Hao Du, Kee Yuan Ngiam +3
In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has alwa…