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Prior-Informed Machine Learning for ...

Shen, Liyue.

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Prior-Informed Machine Learning for Biomedical Imaging and Perception.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prior-Informed Machine Learning for Biomedical Imaging and Perception./
作者:	Shen, Liyue.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	257 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
標題:	Physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29755725
ISBN:	9798357504807

Prior-Informed Machine Learning for Biomedical Imaging and Perception.
Shen, Liyue.

Prior-Informed Machine Learning for Biomedical Imaging and Perception. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 257 p.

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2022.

This item must not be sold to any third party vendors.

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.

ISBN: 9798357504807Subjects--Topical Terms:

516296
Physics.

Prior-Informed Machine Learning for Biomedical Imaging and Perception.
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