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Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis

Jacobs, Joseph George; (2022) Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

In recent years, deep convolutional neural networks (CNNs) have shown promise for improving prostate cancer diagnosis by enabling quantitative histopathology through digital pathology. However, there are a number of factors that limit the widespread adoption and clinical utility of deep learning for digital pathology. One of these limitations is the requirement for large labelled training datasets which are expensive to construct due to limited availability of the requisite expertise. Additionally, digital pathology applications typically require the digitisation of histological slides at high magnifications. This process can be challenging especially when digitising large histological slides such as prostatectomies. This work studies and addresses these issues in two important applications of digital pathology: prostate nuclei detection and cell type classification. We study the performance of CNNs at different magnifications and demonstrate that it is possible to perform nuclei detection in low magnification prostate histopathology using CNNs with minimal loss in accuracy. We then study the training of prostate nuclei detectors in the small data setting and demonstrate that although it is possible to train nuclei detectors with minimal data, the models will be sensitive to hyperparameter choice and therefore may not generalise well. Instead, we show that pre-training the CNNs with colon histology data makes them more robust to hyperparameter choice. We then study the CNN performance for prostate cell type classification using supervised, transfer and semi-supervised learning in the small data setting. Our results show that transfer learning can be detrimental to performance but semi-supervised learning is able to provide significant improvements to the learning curve, allowing the training of neural networks with modest amounts of labelled data. We then propose a novel semi-supervised learning method called Deeply-supervised Exemplar CNNs and demonstrate their ability to improve the cell type classifier learning curves at a much better rate than previous semi-supervised neural network methods.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
URI: https://discovery.ucl.ac.uk/id/eprint/10161619
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