Cancer Classification

Cancer classification aims to accurately categorize different types of cancer using various data sources, including gene expression profiles, medical images (e.g., histopathology, MRI, ultrasound, CT scans), and liquid biopsies. Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs), vision transformers (ViTs), and graph attention networks (GATs), often combined with techniques like transfer learning, ensemble methods, and data augmentation to address challenges posed by limited or imbalanced datasets and inter-site variability. These advancements hold significant promise for improving diagnostic accuracy, enabling earlier and more precise cancer detection, and ultimately leading to better patient outcomes and personalized treatment strategies.