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.
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.
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.