题目：Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
报告人：刘李 研究科学家（深圳大数据研究院）
时间：2020/11/28 下午16:00-17:00
腾讯会议链接：https://meeting.tencent.com/s/EzIyp2VTJUeD
腾讯会议 ID：297 688 458
摘要：Firstly, Deep learning and Machine learning will be briefly introduced. Some hot spots on deep learning will be discussed. Then, our recent work on COVID-19 Lung Ultrasound Multi-symptom Classification will be presented. Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for the lung’s multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative manner. Moreover, a novel lung US dataset named COVID19-LUSMS is built, currently containing 71 clinical patients with 6,836 images sampled from 678 videos. Experimental evaluations show that TSAL can achieve superior performance to the baseline and the state-of-the-art using only 20% data. Qualitatively, visualization of the attention map confirms a good consistency between the model prediction and the clinic knowledge.
个人简介：刘李，1992年出生，2018年博士毕业于法国的格勒诺布尔-阿尔卑斯大学GIPSA实验室，之后在Ryerson University大学做博士后研究，后来入选深圳孔雀计划，现在在深圳大数据研究院工作。是概率论、语音图像处理，多模态融合，医疗图像，机器学习及深度学习等领域年轻一代的专家，已经写出并发表多篇有影响力的论文。
