UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio

no code implementations • 16 Apr 2022 • Jiangeng Chang, Yucheng Ruan, Cui Shaoze, John Soong Tshon Yit, Mengling Feng

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 quickly and consistently detect COVID-19 using acoustic evidence.

Data Augmentation Ensemble Learning

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

no code implementations • 11 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

An Auto-ML Framework Based on GBDT for Lifelong Learning

no code implementations • 29 Aug 2019 • Jinlong Chai, Jiangeng Chang, Yakun Zhao, Honggang Liu

Automatic Machine Learning (Auto-ML) has attracted more and more attention in recent years, our work is to solve the problem of data drift, which means that the distribution of data will gradually change with the acquisition process, resulting in a worse performance of the auto-ML model.

BIG-bench Machine Learning Incremental Learning