Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging

Abstract

Breast cancer remains one of the leading causes of mortality among women globally, and early detection is critical for improving survival rates. Breast MRI, the most sensitive imaging modality for detection, often involves manual review of numerous slices, which is time-intensive and prone to human error. Machine learning (ML) algorithms offer a transformative solution by automating this process, improving efficiency, and enhancing diagnostic accuracy.

In this study, we propose a machine learning approach to enhance breast cancer prediction and diagnosis. We utilize a pre-trained multiscale vision transformer, Wave-ViT, to classify MRI slices as healthy or unhealthy. The model was trained and tested on MRI scans from 922 patients in the Duke Breast Cancer MRI dataset and independently validated on 143 patients from the MAMA-MIA dataset. To ensure high-quality data, both datasets were carefully curated to exclude noisy or mislabeled slices.

The model's performance was evaluated using accuracy, F1-score, precision, recall, and confusion matrices under various experimental conditions. These included randomized training and testing splits using the Fisher-Yates shuffle, exploration of different Wave-ViT variants, and testing across multiple training set configurations. Our approach consistently demonstrated over 94\% accuracy on the external validation dataset, showcasing the potential of machine learning algorithms like Wave-ViT to reduce diagnostic workloads and improve breast cancer detection outcomes.