Keywords: Diagnosis/Prediction, Breast
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.
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