报告人：张小群教授
报告题目：Deep learning-based medical image reconstruction from incomplete data
报告摘要：
（1）Image reconstruction from down-sampled and corrupted measurements, such as fast MRI and low dose CT, is mathematically ill-posed inverse problem. Deep neural network (DNN) has been becoming a prominent tool in the recent development of medical image reconstruction methods. In this talk, I will introduce two work on incorporating classical image reconstruction method and deep learning methods. In the first work, in order to address the intractable inversion of general inverse problems, we propose to train a network to refine intermediate images from classical reconstruction procedure to the ground truth, i.e. the intermediate images that satisfy the data consistence will be fed into some chosen denoising networks or generative networks for denoising and removing artifact in each iterative stage.
（2）In the second work, we proposed a multi-scale DNN for sparse view CT reconstruction, which directly learns an interpolation scheme to predict the complete set of 2D Fourier coefficients in Cartesian coordinates from the given measurements in polar coordinates. In the second work, we proposed an unsupervised deep learning method for LDCT image reconstruction, which does not require any external training data. The proposed method is built on a re-parameterization technique for Bayesian inference via deep network with random weights, combined with additional total variational~(TV) regularization. The experiments on both sparse CT and low dose CT problem show that the proposed method provided state-of-the-art performance.
报告时间：2022年5月20日，上午9：00——12:00
报告形式：腾讯会议；会议号：784 329 846
报告人简介：张小群,上海交通大学教授。武汉大学本科硕士，法国南布列塔尼大学应用数学博士。2007-2010年在美国加州大学洛杉矶分校任访问助理教授，2010加入上海交通大学自然科学研究院和数学科学学院。入选教育部新世纪优秀人才和青年拔尖人才计划。现任Inverse problems and Imaging杂志编委，中国工业与应用数学学会大数据与人工智能专业委员会委员。研究方向：图像科学，机器学习，数据科学与最优化。
