Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis
G. Jha, Manashree Jhawar, Vedant Manelkar, Radhika Kotecha, Ashish Phophalia, Komal Borisagar · 2023
Breast cancer, being among the most widespread terminal illnesses, has the potential to claim the lives of thousands of people each year. Machine learning approaches have shown promise in the biomedical field because of recent advances. However, real-time medical data are hard to obtain, and the number of samples is generally small or unannotated for traditional machine learning approaches to make accurate predictions. The proposed approach uses self-supervised learning to overcome this challenge. Since this model works with unlabeled data and can be downstream from any standard dataset, the proposed approach does not need large labeled datasets. The open-source datasets Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM), and INbreast are used, and samples are arbitrarily selected from them to make a smaller randomized dataset. An accuracy of 96.7% is achieved using bring your own latent (BYOL) as a pretext task on Resnet18 architecture. This chapter demonstrates that the proposed approach surpasses the contemporary state-of-the-art supervised learning methods for breast cancer detection without compromising accuracy. For deep learning models to be used in crucial fields, they should be reliable, and the output should be made in an interpretable manner to understand the reasoning behind a decision made by the model. For this reason, this chapter further demonstrates explainability for deep learning architectures through the use of saliency maps. This approach has the potential to avoid misdiagnosis and assist medical practitioners in turn, saving innumerable precious lives.