会议MLMI 2023发表论文：两阶段多视图低秩稀疏子空间聚类方法探索脑功能与结构之间的关系

论文 “A Novel Two-Stage Multi-View Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship between Brain Function and Structure” 被会议Machine Learning in Medical Imaging (MLMI, MICCAI Workshop)接收

时间：2022-08-01

关键词：脑影像人工智能，文章接收

  硕士研究生康艳晴在Machine Learning in Medical Imaging上面发表题为 “A Novel Two-Stage Multi-View Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship between Brain Function and Structure” 的研究生文章，通讯作者为张枢教授。

  Understanding the relationship between brain function and structure is vital important in the field of brain image analysis. It elucidates the working mechanism of the brain, which will contribute to better understand the brain and simulate the brain-like system. Extensive efforts have been made on this topic, but still far from the satisfactory. The major difficulties are at least two aspects. One is the huge individual difference among the subjects, which makes it hard to obtain stable results at groupwise level, e.g., noise signals can significantly affect the exploring process. The other one is the huge difference between functional and structural features of the brain, both in their pattern and size, which are very different. To alleviate the above problems, in this paper, we propose a two-stage multi-view low-rank sparse subspace clustering (Two-stage MLRSSC) method to jointly study the relationship between brain function and structure and identify the common regions of brain function and structure. The major innovation of proposed Two-stage MLRSSC is that comparable features of brain function and structure can be effectively extracted from low-rank sparse representation, and results are further improved the stability by two-stage strategy. Finally, groupwise-based stable functional and structural common regions are identified for better understanding the relationship. Experimental results shed new ways to explore the brain function and structure, new insights are observed and discussed.

图1：Two-stage MLRSSC 模型框架

参考文献

Zhang, Shu, et al. “A Novel Two-Stage Multi-view Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship Between Brain Function and Structure.” International Workshop on Machine Learning in Medical Imaging. Cham: Springer Nature Switzerland, 2022.