Breast Cancer

Breast cancer research intensely focuses on improving diagnosis, subtyping, and treatment prediction through advanced computational methods. Current efforts leverage deep learning architectures like convolutional neural networks (CNNs), vision transformers, and generative adversarial networks (GANs), often incorporating multi-modal data (e.g., histopathology images, genomic data, MRI scans) and employing techniques like transfer learning and ensemble methods to enhance accuracy and interpretability. These advancements aim to improve early detection, personalize treatment strategies, and ultimately enhance patient outcomes by providing more precise risk assessments and facilitating more effective treatment planning. The integration of explainable AI (XAI) techniques is also a growing focus, aiming to increase the transparency and trustworthiness of AI-driven diagnostic tools.