Mammogram Classification

Mammogram classification research aims to develop accurate and efficient computer-aided diagnosis systems for breast cancer detection, improving both diagnostic speed and accuracy. Current efforts focus on leveraging multi-view information, incorporating textual radiology reports and clinical manifestations, and employing advanced deep learning architectures such as convolutional neural networks (CNNs), transformers (including Swin Transformers), and contrastive learning methods to improve classification performance and interpretability. These advancements hold significant potential to reduce radiologist workload, improve diagnostic accuracy, and ultimately enhance breast cancer screening and patient outcomes.