报告题目：Efficient Computational Models for Medical Image Segmentation and Regression

报告摘要

In medical image analysis, segmentation and regression are two fundamental techniques for understanding an individual image or a population of images. However, conventional methods suffer from high computational costs, especially for high-resolution image volumes; and deep-learning-based approaches have the generalization issue due to insufficient data coverage and limited annotations at the image pixel/voxel level. In this talk, I will present our efficient computational models, which tackle these challenges by fully leveraging available data and decomposing complex models. Applications of our methods include extracting regions of interest or anomalies in MRI and CT scans and understanding brain degeneration and Alzheimer’s Disease.

嘉宾简介

洪义，上海交通大学电院计算机系长聘教轨副教授。于2016年获得美国北卡罗来纳大学教堂山分校博士学位，曾在美国佐治亚大学任长聘教轨助理教授。长期从事医学图像分析与医疗人工智能方向的研究，曾荣获2014 MICCAI青年学者奖，2015-2016北卡罗来纳大学教堂山分校博士论文完成奖学金，主持和参与两项美国自然科学基金项目。

特别感谢本次Webinar主要组织者：

王乾（上海科技大学）