Event Title

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.

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Apr 20th, 10:05 AM Apr 20th, 10:20 AM

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.