32-G449 (MIT Stata Center)
Enhancing Medical Imaging Workflows with Deep Learning
The last few years mark a significant leap in the capability of algorithms with the advent of deep learning. At the same time, hospitals are collecting ever-increasing quantities of medical imaging. Together, deep learning models and big data yield a powerful combination. Integrated in the data workflow, the clinic, or at the bedside, these models have the potential to aid with clinical decision-making, improving efficiency, accuracy, and reliability of patient care. However, at present, there is a critical gap between the researchers who develop deep learning algorithms and the clinicians who utilize the technology to improve patient care. In this work, I focus on several challenges that prevent clinical translation of algorithms. First, vast quantities of data needed to train effective models are often dispersed across institutions and cannot be shared due to ethical, infrastructure, patient privacy concerns. As such, I developed distributed methods of training deep learning models that do not require sharing patient data in multi-institutional collaborative settings. Second, it is not clearly understood how decisions in algorithm design can affect model performance. To this end, I showcase how various training, data, and model parameters can impact algorithm prediction and performance. Lastly, while many algorithms are designed to perform for a single task, there are few pipelines that have multi-faceted functionality needed in patient care. I demonstrate an integrated clinical decision support pipeline for glioma and ischemic stroke that is extensible to other diseases.
Thesis Supervisors:
Jayashree Kalpathy-Cramer, PhD
Associate Professor of Radiology, HMS
Bruce Rosen, MD, PhD
Professor of Health Sciences and Technology, HMS
Thesis Committee Chair:
Elfar Adalsteinsson, PhD
Professor of Health Sciences and Technology (HST) and of Electrical Engineering and Computer Science (EECS), MIT
Thesis Reader:
Bruce R. Fischl, PhD
Professor of Radiology, HMS