Breast Cancer Detection

Breast cancer detection research focuses on developing accurate and efficient computer-aided diagnosis (CAD) systems, primarily using deep learning models to analyze mammograms, ultrasound images, and histopathological images. Current research emphasizes improving model architectures like convolutional neural networks (CNNs), transformers, and generative models, often incorporating techniques such as transfer learning, self-supervised learning, and multi-task learning to enhance performance and address data limitations. These advancements aim to improve diagnostic accuracy, reduce false positives, and ultimately contribute to earlier and more effective breast cancer detection, potentially saving lives and improving patient outcomes.