Radiomic-based deep learning for time-to-event outcome in pulmonary malignancies.

Advances in medical image scanning technologies has allowed for great improvement in medical care, from early tumor detection, to trailered treatments and predicting treatment outcome. However, this has resulted in the generation of huge medical image databases, with thousands of medical image scans per patient that need to be examined by clinicians, making it impractical for clinicians to study all the images. This has led to the development of computer-assisted detection programs to automate part of this process. In this project we aim to develop a computer algorithm that would be used for predicting the treatment outcome for lung cancer patient. This project will focus on a specific group of lung cancer patients treated with focused radiation therapy.

Faculty Supervisor:

Matthew Yedlin

Student:

Ahmed Sigiuk

Partner:

16 Bit Inc.

Discipline:

Engineering - computer / electrical

Sector:

Medical devices

University:

Program: