The application of deep learning to nucleus images for early cancer diagnostics

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Soylemezoglu, Ali Can

Advisor dc:contributor.advisor

• Caroline Uhler.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119709

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119709