University of Illinois Urbana-Champaign

Remedies for challenges in deep learning-based quantitative ultrasound imaging

Soylu, Ufuk

Content Files
SOYLU-DISSERTATION-2024.pdf

Permalink

https://hdl.handle.net/2142/124377

Description

Title
Remedies for challenges in deep learning-based quantitative ultrasound imaging
Author(s)
Soylu, Ufuk
Issue Date
2024-04-24
Director of Research (if dissertation) or Advisor (if thesis)
Oelze, Michael L.
Doctoral Committee Chair(s)
Oelze, Michael L.
Committee Member(s)
  • Do, Minh N.
  • Song, Pengfei
  • Chen, Yun-Sheng
  • Chandrasekaran, Varun
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Deep Learning
  • Quantitative Ultrasound Imaging, Tissue Characterization
  • Data Scarcity
  • Data Mismatch
  • Model Security
Abstract
The thesis of this dissertation suggests that there is a continuous demand for the development of data-efficient and robust deep learning (DL) algorithms in order to promote wider adoption of DL-based algorithms in clinical imaging. This dissertation proposes solutions specifically to address challenges in Deep Learning-based Quantitative Ultrasound (QUS). QUS is a technique that uses ultrasound to estimate the properties of tissues, such as their scattering properties, elasticity or other relevant parameters, for the purposes of classifying tissue state. DL-based QUS aims to improve the accuracy and efficiency of this technique by using deep neural networks to analyze ultrasound image data and extract quantitative information. DL-based QUS approaches have the advantage of being model free and calibration-free if the data is acquired under a scenario of constant image settings from a single machine. However, DL-based QUS faces several challenges, including the need for large amounts of data to train deep neural networks, the variability of ultrasound images due to differences in equipment and imaging settings, and the need for enhancing the understanding of security aspect of DL algorithms. To address these challenges, this dissertation proposes the development of a data-efficient deep learning algorithm called Zone Training, which aims to learn diffraction patterns separately. Additionally, the study introduces a Transfer Function to mitigate variability between ultrasound images resulting from differences in equipment and imaging settings. Building on this, a strategy is developed to "steal" functionality from a victim machine and implement it on a perpetrator machine. Overall, this study highlights the importance of developing data-efficient and robust DL algorithms for the wider adoption of DL-based quantitative ultrasound (QUS). It proposes several remedies to address the challenges faced by this emerging field. Additionally, it demonstrates the ease of stealing the functionality of deep learning models, underscoring the need for security development of these models in clinical settings.
Graduation Semester
2024-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/124377
Copyright and License Information
Copyright 2024 Ufuk Soylu

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois