Titanium bead calibration of deep net classifiers

Cai, William

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

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

Copyright and License Information

Copyright 2024 William Cai

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