Efficient Dataset Creation Framework for Utilizing Complex Large-Scale Clinical Datasets in Machine Learning Applications
Session Number
Project ID: CMPS 02
Advisor(s)
Dr. Yan, Illinois Institute of Technology
Discipline
Computer Science
Start Date
20-4-2022 10:05 AM
End Date
20-4-2022 10:20 AM
Abstract
Artificial Intelligence-based analysis techniques have struggled to make headway in the field of neurological analysis. However, the systems that have been successfully implemented have shaped our modern understanding of neurological interactions. These analysis techniques are largely limited by a lack of robust frameworks for data generation and application, especially with relation to applications that require the usage of large datasets. This project seeks to resolve these issues plaguing modern neurological analysis techniques by outlining a detailed framework for the creation of an efficient registration framework for the formulation and registration of large-scale clinical datasets to significantly improve ease of usage in machine learning-based applications. Future steps include the application of this framework to modern clinical datasets to aid in the development of machine learning-based analysis tools which could significantly improve our understanding of neurological interactions.
Efficient Dataset Creation Framework for Utilizing Complex Large-Scale Clinical Datasets in Machine Learning Applications
Artificial Intelligence-based analysis techniques have struggled to make headway in the field of neurological analysis. However, the systems that have been successfully implemented have shaped our modern understanding of neurological interactions. These analysis techniques are largely limited by a lack of robust frameworks for data generation and application, especially with relation to applications that require the usage of large datasets. This project seeks to resolve these issues plaguing modern neurological analysis techniques by outlining a detailed framework for the creation of an efficient registration framework for the formulation and registration of large-scale clinical datasets to significantly improve ease of usage in machine learning-based applications. Future steps include the application of this framework to modern clinical datasets to aid in the development of machine learning-based analysis tools which could significantly improve our understanding of neurological interactions.