Deep learning for medical image interpretation
Abstract/Contents
- Abstract
- There have been rapid advances at the intersection of deep learning and medicine over the last few years, especially for the interpretation of medical images. In this thesis, I describe three key directions that present challenges and opportunities for the development of deep learning technologies for medical image interpretation. First, I discuss the development of algorithms for expert-level medical image interpretation, with a focus on transfer learning and self-supervised learning algorithms designed to work in low labeled medical data settings. Second, I discuss the design and curation of high-quality datasets and their roles in advancing algorithmic developments, with a focus on high-quality labeling with limited manual annotations. Third, I discuss the real-world evaluation of medical image algorithms with studies systematically analyzing performance under clinically relevant distribution shifts. Altogether this thesis summarizes key contributions and insights in each of these directions with key applications across medical specialties.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Rajpurkar, Pranav Samir |
|---|---|
| Degree supervisor | Liang, Percy |
| Thesis advisor | Liang, Percy |
| Thesis advisor | Bernstein, Michael S, 1984- |
| Degree committee member | Bernstein, Michael S, 1984- |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Pranav Rajpurkar. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/jc097kx0188 |
Access conditions
- Copyright
- © 2021 by Pranav Samir Rajpurkar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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