2026-09-20T03:29:49Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124470 2024-09-16 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Oelze, Michael L 2024-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms The student, William Cai, accepted the attached license on 2024-05-03 at 14:22. The student, William Cai, submitted this Thesis for approval on 2024-05-03 at 14:29. This Thesis was approved for publication on 2024-05-03 at 14:38. DSpace SAF Submission Ingestion Package generated from Vireo submission #20777 on 2024-09-16 at 00:38:07 Titanium bead calibration of deep net classifiers Cai, William 2024-05-03 Ultrasound Machine Learning Deep Learning Domain Adaptation 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\%. Text eng https://hdl.handle.net/2142/124470 Copyright 2024 William Cai M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng