Self-supervised adversarial adaptation network for breast cancer detection

Abstract

Breast cancer is the most commonly diagnosed cancer worldwide, and early detection is essential for reducing mortality rates. Digital mammography is currently the best standard for early detection, as it can assist physicians in treating the disease. However, inaccurate diagnoses from mammography are common and can lead to patients undergoing unnecessary tests and treatments. To address this challenge, deep-learning techniques have shown promising results in improving the accuracy and reliability of breast cancer detection. However, existing methods face two primary challenges: the lack of the annotated data, and the inability to adapt to new data domains. In this paper, we propose SelfAdaptNet to address these issues. Specifically, SelfAdaptNet employs self-supervised learning techniques, such as Bootstrap Your Own Latent (BYOL) and Simple Framework for Learning of Visual Representations (SimCLR), to tackle the problem of limited annotated data. Additionally, the adversarial technique is used to address the problem of domain shift. By successfully reducing domain disparities, this strategy enhances the model’s adaptability and robustness across a variety of clinical scenarios. Overall, our contributions offer a more effective and flexible approach for early breast cancer detection, and experimental results demonstrate that SelfAdaptNet can produce promising results as compared with other methods.