Interpretable Small Training Set Image Segmentation Network Originated from Multi-grid Variational Model
数学专题报告
报告题目(Title)：Interpretable Small Training Set Image Segmentation Network Originated from Multi-grid Variational Model
报告人(Speaker)：郭卫红 教授 (美国凯斯西楚大学)
地点(Place)：后主楼1220
时间(Time)：2023年6月27日(周二), 9:30-10:30
邀请人(Inviter)：刘君
报告摘要
The main objective of image segmentation is to divide an image into homogeneous regions for
further analysis. This is a significant and crucial task in many applications such as medical imaging. Deep learning (DL) methods have been proposed and widely used for image segmentation. However, these methods usually require a large amount of manually segmented data as training data and suffer from poor interpretability (known as the black box problem). The classical Mumford-Shah (MS) model is effective for segmentation and provides a piece-wise smooth approximation of the original image. In this paper, we replace the hand-crafted regularity term in the MS model with a data adaptive generalized learnable regularity term and use a multi-grid framework to unroll the MS model and obtain a variational model-based segmentation network with better generalizability and interpretability. This approach allows for the incorporation of learnable prior information into the network structure design. Moreover, the multi-grid framework enables multi-scale feature extraction
and offers a mathematical explanation for the effectiveness of the U-shaped network structure.
in producing good image segmentation results. Due to the proposed network originates from a variational model, it can also handle small training sizes. Our experiments on the REFUGE dataset, the White Blood Cell image dataset, and 3D thigh muscle magnetic resonance
(MR) images demonstrate that even with smaller training datasets, our method yields better segmentation results compared to related state of the art segmentation methods。
主讲人简介
郭卫红现任美国凯斯西楚大学应用数学教授和系主任。她1999年从中央民族大学本科毕业后被保送北京师范大学。2017年从美国佛罗里达大学应用数学博士毕业，同时获得统计专业硕士。她在图像处理和反问题领域得到了很多重要的原创性科研成果。已经在 SIAM J. Imaging Sciences, Inverse Problems and Imaging, Information Sciences, Journal of Computational and Applied Mathematics, IEEE Transactions on Image Processing, EEE Transactions on Circuits and Systems for Video Technology, Magnetic Resonance Imaging and Magnetic Resonance in Medicine 等重要期刊发表将近50篇论文。她担任过超过20个国际会议和杂志的审稿人，自2012年担任国际杂志Inverse Problems and Imaging 的编委，曾担任国际杂志 International Journal of Biomedical Imaging 的客座编辑。她也组织过多次学术会议。
