Relieving label requirements through weakly supervised learning
Name
1192539594-MIT.pdf
Size
3.61 MB
Format
Adobe PDF
Checksum (MD5)
ad05cab3d8e4c6b451b06a9ce12e0b11
Author(s)
Chen, Bryan,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
Jayashree Kalpathy-Cramer and Elfar Adalsteinsson.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Precise annotation of data is a time-consuming and costly process, yet many models rely on having large amounts of labeled data for training. Many domains, including healthcare, have access to large amounts of unlabeled data, motivating groups to leverage this data for training their machine learning models. This thesis firstly investigates the correlation between the size of a medical dataset and the performance of models for different machine learning tasks, such as classification, detection, and segmentation. This thesis further explores the utility of training with additional unlabeled data by adopting various weakly supervised techniques. Finally, while comparing these techniques to supervised learning baselines, we highlight the trade-offs between using smaller labeled data sizes and model performances.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 81-86).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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