Thesis
Improving deep-learning segmentation performance in 3D neuroimaging with minimal manual annotations
- Abstract:
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One of the current challenges in applying machine learning to medical images is the difficulty in obtaining labelled training data. While medical images themselves are often available, generating high-quality training labels for them is time-consuming and often requires a trained clinician. This problem is particularly acute in 3D segmentation, which generally requires detailed voxel-by-voxel segmentation maps.
This thesis proposes a series of image analysis methods to leverage a...
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- Files:
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(Preview, Dissemination version, pdf, 10.5MB, Terms of use)
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Authors
Contributors
+ Papageorghiou, A
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Women's & Reproductive Health
- Sub department:
- Women's & Reproductive Health
- Oxford college:
- Pembroke College
- Role:
- Supervisor
+ Noble, J
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Engineering Science
- Sub department:
- Institute of Biomedical Engineering
- Research group:
- Biomedical Image Analysis
- Oxford college:
- Pembroke College
- Role:
- Supervisor
+ Namburete, AI
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Computer Science
- Sub department:
- Computer Science
- Oxford college:
- Pembroke College
- Role:
- Supervisor
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- http://dx.doi.org/10.13039/501100000266
- Funding agency for:
- Venturini, L
- Grant:
- EP/L016052/1
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Subjects:
- Deposit date:
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2022-04-13
- ARK identifier:
Terms of use
- Copyright holder:
- Venturini, L
- Copyright date:
- 2021
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