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
Language
eng
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\%.
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