Manifold Topological Deep Learning for Biomedical Data

Principal Investigator

Name
Xiang Liu

Degrees
Ph.D

Institution
Department of Mathematics, Michigan State University

Position Title
Visiting Assistant Professor

Email
liuxia98@msu.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-1396

Initial CDAS Request Approval
Mar 5, 2025

Title
Manifold Topological Deep Learning for Biomedical Data

Summary
Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging as a promising paradigm in data science. However, TDL has not been developed for data on differentiable manifolds, including images, due to the challenges posed by differential topology. We address this challenge by introducing manifold topological deep learning (MTDL) for the first time. We already evaluated our MTDL model on 17 2D and 3D datasets of the MedMNIST v2 database. In order to assess the clinical applicability of our model for medical image analysis, I want to apply for some datasets from NLST.

Aims

We aim to evaluate our developed model by testing it on real-world clinical datasets.

Collaborators

Zhe Su
suzhe@msu.edu
Department of Mathematics
Michigan State University

Yongyi Shi
shiy11@rpi.edu
Biomedical Imaging Center
Rensselaer Polytechnic Institute

Yiying Tong
ytong@msu.edu
Computer Science and Engineering
Michigan State University

Ge Wang
wangg6@rpi.edu
Biomedical Imaging Center
Rensselaer Polytechnic Institute

Guo-Wei Wei
weig@msu.edu
Department of Mathematics
Department of Electrical and Computer Engineering
Department of Biochemistry and Molecular Biology
Michigan State University