Machine Learning Methods for Image-based Personalized Cancer Screening

While AI has the potential to transform patient care, the development of equitable clinical AI models and their translation to hospitals remains difficult. From a computational perspective, these tools must deliver consistent performance across diverse populations and adapt to diverse clinical needs, while learning from biased and scarce data. Moreover, the development of tools relies on our capacity to balance clinical AI utility and patient privacy concerns. In this thesis, I will discuss our contributions in addressing the above challenges in three areas: 1) cancer risk assessment from imaging, 2) personalized screening policy design and 3) private data sharing through neural obfuscation. I have demonstrated that our clinical models offer significant improvements over the current standard of care across globally diverse patient populations. The models now underlie prospective clinical trails.

Name thesis:degree_name

Doctoral

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

2022

Author dc:creator

• Yala, Adam

Advisor dc:contributor.advisor

• Barzilay, Regina

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144581

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144581