Towards a Reliable Deep Learning Framework for Prostate Cancer Diagnosis using Ultrasound
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Authors
Harmanani, Mohamed
Date
2024-09-18
Type
thesis
Language
eng
Keyword
deep learning , uncertainty estimation , learning from label noise , prostate cancer , ultrasound
Alternative Title
Abstract
Reliable prostate cancer (PCa) detection using ultrasound is crucial for improving patient outcomes. Developing deep learning (DL) models for PCa detection is hindered by noisy labels and cancer heterogeneity. The purpose of this work is to develop a clinically applicable framework for DL-based detection of PCa from ultrasound that is robust to noise and uncertainty inherent to ultrasound images and their associated gold standard pathology labels.
We begin by conducting a comprehensive evaluation of various DL baselines to assess their performance on noisy ultrasound data. We explore vision transformers for efficient feature extraction, followed by noise-resistant fine-tuning with multiple-instance learning (MIL). We then propose a novel multi-objective loss function that combines predictions from the extractor and the MIL aggregator, ensuring robust performance by maximizing agreement.
Prostate ultrasound data is severely imbalanced in favor of benign cores. We propose TRUSWorthy, a unified framework that integrates self-supervised learning and MIL with an ensemble strategy that trains on diverse subsets of the majority class to enhance performance and uncertainty calibration. To our knowledge, this is the first framework to address noisy labels, data heterogeneity, and imbalance in PCa detection simultaneously.
TRUSWorthy outperforms state-of-the-art methods in accuracy and uncertainty calibration. As model confidence increases, we also observe larger improvements over the baseline in both qualitative and quantitative performance. Our findings show that ensemble diversity is an effective strategy for reliable PCa detection and uncertainty estimation. They further suggest that an integrated solution has the potential to address both noise and uncertainty during clinical deployment.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International