2026-09-20T20:15:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1131302026-06-06T00:49:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay.
Locascio, Nicholas (Nicholas J.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-01-12T20:58:14Z
2018-01-12T20:58:14Z
2017
http://hdl.handle.net/1721.1/113130
1017567458
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"June 2017." Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 37).
Breast cancer is the most common cancer among women worldwide. Today, the vast majority of breast cancers are diagnosed from screening mammography. Multiple randomized clinical studies have demonstrated that screening mammography can help reduce the number of deaths from breast cancer among women ages 40 to 74, especially for those over age 50 [4], and can provide women diagnosed with breast cancer more options for less aggressive treatment [7]. Screening mammography is the first entry into the funnel of clinical mammography. A screening mammogram can result in a suspicious finding, leading the patient to receive additional imaging, and even surgical biopsy if the additional imaging. Screening mammography, as the first part of this funnel, is a place for machine learning to have impact on the largest amount of patients. In this work, we apply machine learning models to tasks in clinical mammography such as density estimation, and Bi-Rads prediction.
by Nicholas Locascio.
M.Eng.
37 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Deep learning for clinical mammography screening
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2d8c3673-fa55-47e9-88f3-edd10a827ed5">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Deep learning for clinical mammography screening</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2017</PublicationDate>
<Authors>
<Author>
<DisplayName>Locascio, Nicholas (Nicholas J.)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Breast cancer is the most common cancer among women worldwide. Today, the vast majority of breast cancers are diagnosed from screening mammography. Multiple randomized clinical studies have demonstrated that screening mammography can help reduce the number of deaths from breast cancer among women ages 40 to 74, especially for those over age 50 [4], and can provide women diagnosed with breast cancer more options for less aggressive treatment [7]. Screening mammography is the first entry into the funnel of clinical mammography. A screening mammogram can result in a suspicious finding, leading the patient to receive additional imaging, and even surgical biopsy if the additional imaging. Screening mammography, as the first part of this funnel, is a place for machine learning to have impact on the largest amount of patients. In this work, we apply machine learning models to tasks in clinical mammography such as density estimation, and Bi-Rads prediction.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>