2026-09-19T04:31:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1526682023-11-03T03:08:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
You, Sixian
Gerlach, Connor Michael
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-11-02T20:07:05Z
2023-11-02T20:07:05Z
2023-09
2023-09-21T14:26:34.874Z
https://hdl.handle.net/1721.1/152668
This work aims to survey and advance the state of the art in methods for biomedical imaging and disease diagnosis. We demonstrate the generation of non-diffracting beams using Lee Holography, and argue that the rich depth information made possible by these beams is well-suited for machine learning applications, where 2D images can contain 3D contextual information without the added computational overhead of performing 3D convolutions. We begin with a review of important non-diffracting beams in the existing literature, and proceed to discuss the necessary experimental design for their generation. We then demonstrate the experimental generation of these beams, including the novel generation of a rotating beam and needle beam via Lee holography. This is followed by the presentation and analysis of a particular semi-supervised machine learning method, contrastive learning, and a novel demonstration of how transfer learning can further improve the representations made by contrastive learning.
S.M.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Computational Methods for Biomedical Imaging
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>This work aims to survey and advance the state of the art in methods for biomedical imaging and disease diagnosis. We demonstrate the generation of non-diffracting beams using Lee Holography, and argue that the rich depth information made possible by these beams is well-suited for machine learning applications, where 2D images can contain 3D contextual information without the added computational overhead of performing 3D convolutions. We begin with a review of important non-diffracting beams in the existing literature, and proceed to discuss the necessary experimental design for their generation. We then demonstrate the experimental generation of these beams, including the novel generation of a rotating beam and needle beam via Lee holography. This is followed by the presentation and analysis of a particular semi-supervised machine learning method, contrastive learning, and a novel demonstration of how transfer learning can further improve the representations made by contrastive learning.</Abstract>
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