Prior-informed machine learning for biomedical imaging and perception
Abstract/Contents
- Abstract
- Deepening our understanding of human health is more important than ever before for addressing real-world challenges in biomedicine and healthcare, especially with the recent pandemic. My research focuses on AI in medicine, to develop efficient ML models for biomedical imaging and perception for addressing clinically important problems. In this thesis, I will first explore the challenges in this emerging field and then present the two majors lines of my PhD research work. First, I will introduce my work on AI in biomedical imaging. Specifically, I will discuss how to integrate different kinds of prior knowledge to develop reliable data-efficient ML models, by exploiting the personalized priors, population priors and physics priors. With the prior-informed ML models, the proposed approaches can be applied to various applications including sparse-sampling CT and MRI image reconstruction, X-ray projection synthesis, and Cryo-EM imaging. These techniques show significant potential to impact cancer imaging and treatment. Second, I will introduce my work on AI in image perception. I will discuss how to develop ML-driven perception models that can adapt to the unique characteristics of biomedical data. Specifically, I will present a self-attention-guided ML model for quantitative image perception of fetal brain MRI images. This includes a cross-institute validation involving four U.S. clinical centers and a Turkish institute. This work demonstrates the ability of the developed ML model to characterize in utero neurodevelopment for anomaly detection in real-world clinical deployments.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Shen, Liyue |
|---|---|
| Degree supervisor | Pauly, John (John M.) |
| Degree supervisor | Xing, Lei |
| Thesis advisor | Pauly, John (John M.) |
| Thesis advisor | Xing, Lei |
| Thesis advisor | Wetzstein, Gordon |
| Degree committee member | Wetzstein, Gordon |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Liyue Shen. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/hb714qz0132 |
Access conditions
- Copyright
- © 2022 by Liyue Shen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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