Synthetic data augmentation for retinoblastoma using diffusion

Author

Andrew Thompson, Mississippi State UniversityFollow

Issuing Body

Mississippi State University

Advisor

Perkins, Andy

Committee Member

Shahram, Rahimi

Committee Member

Jingdao, Chen

Date of Degree

5-16-2025

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Computer Science (Research Computer Science)

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Many AI models rely on large and high quality datasets for optimal training. In certain cases, data can be difficult or expensive to obtain, making training difficult. Rare medical conditions are one of these cases. Datasets for retinoblastoma are severely lacking in quantity. Diffusion has been used to create synthetic data in the industrial, medical, and financial domains. By applying the latest Diffusion methods to retinoblastoma, this work seeks to improve predictive model performance on identifying retinoblastoma.

Recommended Citation

Thompson, Andrew, "Synthetic data augmentation for retinoblastoma using diffusion" (2025). Theses and Dissertations. 6589.
https://scholarsjunction.msstate.edu/td/6589