Improving breast cancer risk assessment with image-based deep learning models
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/121635 |
| _version_ | 1826211436282511360 |
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| author | Portnoi, Tally E. |
| author2 | Regina Barzilay. |
| author_facet | Regina Barzilay. Portnoi, Tally E. |
| author_sort | Portnoi, Tally E. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T15:05:51Z |
| format | Thesis |
| id | mit-1721.1/121635 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:05:51Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1216352019-09-14T03:01:34Z Improving breast cancer risk assessment with image-based deep learning models Portnoi, Tally E. Regina Barzilay. 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. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 59-60). Discriminative models for breast cancer risk prediction are needed in order to provide personalized patient care. Existing breast cancer risk models incorporate information about breast tissue using imaging biomarkers such as density scores. However, these imaging biomarkers are limited in that they suffer from variability in radiologists' assessments and they reduce the rich information contained in the image down to a single number. In this thesis, I present deep learning models that predict breast cancer risk directly from full images, specifically breast MRIs and mammograms. Our image-based deep learning models out-perform existing breast cancer risk models and our own risk-factor-only models. These results demonstrate that full images contain subtle but significant indicators of risk not captured by traditional risk factors, and that deep learning models can learn these patterns directly from the data. by Tally E. Portnoi. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-07-15T20:29:48Z 2019-07-15T20:29:48Z 2018 2018 Thesis https://hdl.handle.net/1721.1/121635 1098179577 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 60 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Portnoi, Tally E. Improving breast cancer risk assessment with image-based deep learning models |
| title | Improving breast cancer risk assessment with image-based deep learning models |
| title_full | Improving breast cancer risk assessment with image-based deep learning models |
| title_fullStr | Improving breast cancer risk assessment with image-based deep learning models |
| title_full_unstemmed | Improving breast cancer risk assessment with image-based deep learning models |
| title_short | Improving breast cancer risk assessment with image-based deep learning models |
| title_sort | improving breast cancer risk assessment with image based deep learning models |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/121635 |
| work_keys_str_mv | AT portnoitallye improvingbreastcancerriskassessmentwithimagebaseddeeplearningmodels |