学术报告

【online】 Semi-supervised learning for neuropathology imaging: improve reliability against bias

发布人:发布时间: 2022-06-21

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题目: Semi-supervised learning for neuropathology imaging: improve reliability against bias

报告人:Chao Wang (南方科技大学)

摘要: The need for manual and detailed annotations limits the applicability of supervised deep learning algorithms in medical image analyses, specifically in the field of pathology. Semi-supervised learning (SSL) provides an effective way for leveraging unlabeled data to relieve the heavy reliance on the amount of labeled samples when training a model. Although SSL has shown good performance, the performance of recent state-of-the-art SSL methods on pathology images is still under study. The problem for selecting the most optimal data to label for SSL is not fully explored. To tackle this challenge, we propose a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for annotation queries to expand the diversity and volume of the labeled set quickly. Lastly, we explore the imbalance issue of the neuropathology and developed a smoothed adaptive weighting for imbalanced semi-supervised learning.

方式:腾讯会议  ID:895-499-472

时间:20220622日 下午2:00-3:30

报告人简介: 王超,南方科技大学统计与数据科学系助理教授,在2018年毕业于香港中文大学数学系,在美国德州大学和加州大学共积累近三年海外博士后工作经验。其研究方向主要为图像处理、科学计算与交叉学科的数据科学,并在理论和算法上取得了一些创新性的研究成果。近年来以第一作者或通讯作者在 TIP, SISC, SIIMS, ICML, IP等国际期刊及学术会议上发表了十几篇论文。在2017年获得第十五届中国工业与应用数学学会(CSIAM)年会最佳论文,并在2018与2020年分别获得全球SIAM学生/青年学者会议基金奖。在2021年王超作为主要成员参与了香港研资局科研基金项目。在2022年3月王超作为图像处理领域权威国际会议SIAM Conference on Imaging Science分会场举办人,主持一场生物医学图像研讨会。


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