- Author
-
M. Lucas
- Title
- Deep learning for histopathology of the lower urinary tract
- Supervisors
- Co-supervisors
- Award date
- 21 January 2021
- Number of pages
- 105
- ISBN
- 9789463327176
- Document type
- PhD thesis
- Faculty
- Faculty of Medicine (AMC-UvA)
- Abstract
-
Histopathology is the cornerstone in the diagnosis and treatment-decision making for many cancers. The examination consists of scoring two aspects of the tumor, the tumor aggressiveness (grade) and the tumor location and spread (stage). A significant drawback of this examination is the high inter-observer variation in both grade and stage of the tumor, potentially leading to suboptimal treatment.
In this thesis, we try to improve the histopathological grade examination for prostate and bladder cancer by using deep learning. In this case, automated decision making tools are trained on histopathology slides labeled by pathologists. Nonetheless, these techniques are still subjected to the high inter-observer variation, and to that end we also try to automatically predict the long-term outcome of bladder cancer patients. - Note
- Please note that the acknowledgements section is not included in the thesis downloads.
- Persistent Identifier
- https://hdl.handle.net/11245.1/15c622f0-b12e-4862-b098-cd073c3ff450
- Downloads
-
Thesis
Front matter
1: Introduction
2: Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies
3: Toward automated in vivo bladder tumor stratification using Confocal Laser Endomicroscopy
4: Automated detection and grading of non-muscle invasive urothelial cell carcinoma of the bladder
5: Recurrence in non-muscle invasive bladder cancer patients: External validation of the EORTC, CUETO and EAU risk tables and towards a non-linear survival model
6: Deep learning based recurrence prediction in patients with non-muscle invasive bladder cancer
7: General discussion
Summary; Samenvatting; Portfolio; Author contributions; Author affiliations; About the author
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