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University of Illinois Urbana-Champaign

Synthetic data generation pipeline to effectively train deep learning augmented super-resolution ultrasound imaging

Abstract

dc:description

Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01

Degree

thesis:*
Name thesis:degree_name
M.S.
Level thesis:degree_level
Thesis
Discipline thesis:degree_discipline
Electrical & Computer Engr
Grantor
University of Illinois Urbana-Champaign
Year dc:date
2025

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Katakam, Swathi
Contributors dc:contributor
  • Chen, Yun-sheng

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • Copyright 2025 Swathi Katakam
Language dc:language
en, eng

Identifiers

dc:identifier.*
Handle dc:identifier
https://hdl.handle.net/2142/129788
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/129788

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Katakam, Swathi. Synthetic data generation pipeline to effectively train deep learning augmented super-resolution ultrasound imaging. Thesis thesis, University of Illinois Urbana-Champaign, 2025. https://hdl.handle.net/2142/129788