Breast Lesion

Breast lesion analysis focuses on accurately identifying and classifying benign and malignant breast masses using various imaging modalities like mammograms, ultrasound, and MRI. Current research heavily utilizes machine learning, particularly deep learning architectures such as convolutional neural networks, to analyze image features (including kinetic curves from DCE-MRI) and improve diagnostic accuracy, often addressing challenges like class imbalance and limited annotated data through techniques like semi-supervised learning and data augmentation. These advancements aim to improve the speed and accuracy of breast cancer diagnosis, potentially leading to earlier interventions and better patient outcomes.