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  4. Polycystic Kidney Disease MRI Classification and Detection

Polycystic Kidney Disease MRI Classification and Detection

File(s)
Krasnoff_cornell_0058O_10837.pdf (3.21 MB)
Permanent Link(s)
https://doi.org/10.7298/vsg2-xb55
https://hdl.handle.net/1813/70226
Collections
Cornell Theses and Dissertations
Author
Krasnoff, Will
Virk, Ishan Singh
Abstract

This proposal outlines the following topics regarding the project of classifying and detecting PKD1 and PKD2 within MRIs: Firstly, it discusses the background of the problem and introduces the premise of the research. Second, it compares related works and the results/outcomes from our methods. It also outlines the limitations and scope of the project parameters. Lastly, it covers the future work that is proposed to improve results of this project. The scope of this project is exploratory - the objective was to determine the potential to advance the work done in using Deep Learning to assist the radiological work in the domain of PKD. This is the final report of our Specialization Project, a two-semester project required for the Connective Media and Health Tech Master program. This project was a two person research-oriented project done under the guidance of a faculty member at Cornell Tech and Weill Cornell Medical School.

Description
44 pages
Date Issued
2020-05
Keywords
Classification
•
Deep Learning
•
MRI
•
PKD
•
Segmentation
•
Machine Learning
•
CNN
Committee Chair
Azenkot, Shiri
Committee Member
Estrin, Deborah
Degree Discipline
Information Science
Degree Name
M.S., Information Science
Degree Level
Master of Science
Type
dissertation or thesis
Link(s) to Catalog Record
https://catalog.library.cornell.edu/catalog/13254471

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