Few-shot Learning for Differentiation of Malignant and Benign Breast Cancer Lesions Using Dynamic Contrast-enhanced MRI

Motivation: Breast cancer (BrCa) is the most prevalent malignancy among women. MRI is a useful tool for BrCa early detection and characterization. However, high false-positive rates can lead to unnecessary biopsies and patient distress. To enhance diagnostic accuracy, deep learning presents a promising avenue, but training deep neural networks (DNN) requires a large, annotated dataset. Goal(s): Introduce a novel method for BrCa classification, utilizing a minimally labeled dataset. Approach: We employ a few-shot learning (FSL) approach to differentiate between benign and malignant breast tumors. Results: Our FSL-based model significantly surpasses the diagnostic performance of trained radiologists in breast cancer classification (p < 0.0001). Impact: Our FSL model streamlines machine learning by reducing data labeling needs outperforms radiologists in detecting breast cancer, and could reduce unnecessary biopsies, sparing patients from potential harm.