Work

Statistical Learning Of Multimodal Medical Imaging Data With Block-Wise Missing Structure

Year: 2025

Type: article

Abstract: This dissertation addresses the pervasive challenge of block-wise missingness in multimodal medical imaging data. To address this, we propose two novel frameworks: BaMM and IMMR. Through rigorous theoretical development, extensive simulations, and real-world applications on the ADNI dataset, these frameworks demonstrate competitive performance in prediction accuracy and uncertainty quantification. These contributions significantly advance the field of multi-modal medical data analysis by providing effective solutions for handling missing data and improving the reliability of diagnostic predictions.

Source: SMU Scholar (Southern Methodist University)

Author: Lu, Yifan

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7015449571

FWCI:

Cites:

Cited by:

Related to: