Enhancing Breast Cancer Detection Through Combination of Contrastive Learning and Adversarial Domain Adaptation

The most common cancer diagnosed worldwide is breast cancer and early detection is essential for reducing mortality. The best standard for early detection of breast cancer is digital mammography, which can aid physicians in treating the illness when it is still curable. However, inaccurate mammography diagnoses are frequent and can cause patients to undergo unnecessary examinations and therapies. This study aims to explore deep-learning techniques that can be utilized to implement and train a model to identify breast cancer cases in mammograms. Current deep learning-based diagnostic techniques are hindered by two fundamental issues: the expensive and time-consuming task of data annotation, and the lack of being adaptable to new or different data domains. This thesis uses cutting-edge deep learning methods to address these issues. We use self-supervised learning techniques like Bootstrap Your Own Latent (BYOL) and Simple Framework for Contrastive Learning of Visual Representations (SimCLR) to address the problem of scarce annotated data. These methods are contrastive learning methods which are a subset of self-supervised learning and have recently emerged as a crucial component for learning visual representations. These methods significantly reduce the need for labeled data while maintaining high model performance. Contrastive learning is still substantially unknown in the context of domain adaptation. These techniques have faced difficulties in their practical implementations due to issues with biased datasets that cause domain shifts. Domain shift occurs when distributions of data across domains have differences. In this research, we propose a technique to combine adversarial and contrastive learning to address the domain shift problem. By successfully reducing domain disparities, this strategy increases the model’s adaptability and robustness in a variety of clinical scenarios. Overall, our contributions provide an approach for early breast cancer detection that is more effective and flexible and has the potential to have significant effects on medical imaging diagnostics.

Name thesis:degree_name

M.A.Sc.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Windsor

Year dc:date.issued

2023

Author dc:creator

• Torabi, Mahnoosh

Advisor dc:contributor.advisor

• Jonathan Wu

Contributors dc:contributor

• scholarship@uwindsor.ca

Licence

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en_CA

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14776/8406

OAI identifier oai:identifier

oai:uwindsor.scholaris.ca:20.500.14776/8406