University of Illinois Urbana-Champaign

Titanium bead calibration of deep net classifiers

Cai, William

Content Files
CAI-THESIS-2024.pdf

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https://hdl.handle.net/2142/124470

Description

Title
Titanium bead calibration of deep net classifiers
Author(s)
Cai, William
Issue Date
2024-05-03
Director of Research (if dissertation) or Advisor (if thesis)
Oelze, Michael L
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • ultrasound
  • machine learning
  • deep learning
  • domain adaptation
Abstract
With the rapid development of computational power in the last decade, the field of deep learning and its applications have advanced greatly. The field of medical ultrasound specifically has greatly benefited from the integration of deep learning; however, it is often hindered by domain differences between training and deployment. In order to solve this issue, references phantoms have been used in the past to calibrate domains; however, those are unable to calibrate differences within the tissue especially when large differences exist between training and testing domains due to tissue attenuation. In this work, we examine the use of an in situ titanium bead and its potential use as a calibration signal to allow deep learning models to generalise between training and testing domains. It is determined that the calibration using this bead can lead to improvements in classifier accuracy from 50\% up to 93\%.
Graduation Semester
2024-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/124470
Copyright and License Information
Copyright 2024 William Cai

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