Search Results for author: Mengling Feng

Found 8 papers, 0 papers with code

Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals

no code implementations18 Sep 2021 Xiang Lan, Dianwen Ng, Shenda Hong, Mengling Feng

In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients.

Contrastive Learning Self-Supervised Learning +1

DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021

no code implementations11 Jul 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.

Data Augmentation Ensemble Learning

Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms

no code implementations14 May 2021 Hao Du, Melissa Min-Szu Yao, Liangyu Chen, Wing P. Chan, Mengling Feng

In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms.

Graph Classification Graph Learning

Zoom in to where it matters: a hierarchical graph based model for mammogram analysis

no code implementations16 Dec 2019 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.

Classification General Classification +3

Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey

no code implementations22 Jul 2019 Si-Qi 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 delivering personalized care.

A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care

no code implementations14 Jan 2019 Ziyuan Pan, Hao Du, Kee Yuan Ngiam, Fei Wang, Ping Shum, Mengling Feng

Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.

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