The application of deep learning to nucleus images for early cancer diagnostics
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119709 |
| _version_ | 1811093860452925440 |
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| author | Soylemezoglu, Ali Can |
| author2 | Caroline Uhler. |
| author_facet | Caroline Uhler. Soylemezoglu, Ali Can |
| author_sort | Soylemezoglu, Ali Can |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T15:51:51Z |
| format | Thesis |
| id | mit-1721.1/119709 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:51:51Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197092019-04-12T19:27:21Z The application of deep learning to nucleus images for early cancer diagnostics Soylemezoglu, Ali Can Caroline Uhler. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 69-72). Cancer remains a major concern for patients and early diagnosis can go a long way in treating patients. Current cancer diagnosis usually involves a pathologist looking at tissue slices of patients for specific features associated with cancer prognosis such as nuclear morphometric measures. However, early diagnosis remains a major challenge. Recent studies have shown that changes in fibroblast nuclei play a critical role in the early development of cancer. In addition, it is crucial that computational models are capable of justifying themselves when used in critical decisions such as diagnosing a patient with cancer. In this thesis, we use machine learning techniques on two dimensional nuclei images to show that computational models are capable of presenting human interpretable features as a means of justifying themselves. In addition, we use machine learning techniques on volumetric images of nuclei of cells in a co-culture model that represents the cancer tissue microenvironment to study changes the fibroblasts undergo. These studies pave the way for various approaches to early disease diagnosis. by Ali Can Soylemezoglu. M. Eng. 2018-12-18T19:46:43Z 2018-12-18T19:46:43Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119709 1078409345 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 72 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Soylemezoglu, Ali Can The application of deep learning to nucleus images for early cancer diagnostics |
| title | The application of deep learning to nucleus images for early cancer diagnostics |
| title_full | The application of deep learning to nucleus images for early cancer diagnostics |
| title_fullStr | The application of deep learning to nucleus images for early cancer diagnostics |
| title_full_unstemmed | The application of deep learning to nucleus images for early cancer diagnostics |
| title_short | The application of deep learning to nucleus images for early cancer diagnostics |
| title_sort | application of deep learning to nucleus images for early cancer diagnostics |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119709 |
| work_keys_str_mv | AT soylemezoglualican theapplicationofdeeplearningtonucleusimagesforearlycancerdiagnostics AT soylemezoglualican applicationofdeeplearningtonucleusimagesforearlycancerdiagnostics |