Medical Image Analysis with Less and Noisy Labels
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Guo, ErjianAbstract
Deep learning has revolutionized medical image analysis, but its success heavily relies on supervised training with large, clean-labeled datasets. Acquiring such datasets is both costly and labor-intensive, often requiring expert annotations from medical professionals. To address ...
See moreDeep learning has revolutionized medical image analysis, but its success heavily relies on supervised training with large, clean-labeled datasets. Acquiring such datasets is both costly and labor-intensive, often requiring expert annotations from medical professionals. To address this challenge, Medical image analysis with Less and Noisy labels (Med-LN) has emerged as a promising solution, enabling deep learning on less or noisy labeled datasets. However, the challenges posed in the medical imaging domain remain under-explored. In this thesis, we first address the issue of less labeled datasets by leveraging both labeled and unlabeled data. Then, we explore the common issue of noisy labels in medical datasets. Specifically, we focus on three key tasks in medical image analysis under Med-LN conditions: medical image enhancement, medical image segmentation, and medical visual question answering.
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See moreDeep learning has revolutionized medical image analysis, but its success heavily relies on supervised training with large, clean-labeled datasets. Acquiring such datasets is both costly and labor-intensive, often requiring expert annotations from medical professionals. To address this challenge, Medical image analysis with Less and Noisy labels (Med-LN) has emerged as a promising solution, enabling deep learning on less or noisy labeled datasets. However, the challenges posed in the medical imaging domain remain under-explored. In this thesis, we first address the issue of less labeled datasets by leveraging both labeled and unlabeled data. Then, we explore the common issue of noisy labels in medical datasets. Specifically, we focus on three key tasks in medical image analysis under Med-LN conditions: medical image enhancement, medical image segmentation, and medical visual question answering.
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Date
2025Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Electrical and Information EngineeringAwarding institution
The University of SydneyShare