Cancer Prediction

Cancer prediction research aims to develop accurate and interpretable models for early and reliable cancer diagnosis, improving patient outcomes and treatment strategies. Current efforts focus on leveraging machine learning algorithms like gradient boosting, variational autoencoders, and graph neural networks, often combined with techniques to address data limitations (e.g., data augmentation, active learning) and enhance model explainability (e.g., SHAP values, feature selection). These advancements are crucial for translating AI-driven cancer detection into clinical practice, particularly in improving the accuracy and reliability of predictions, especially for challenging cases with subtle imaging features or complex genomic data. The ultimate goal is to provide clinicians with trustworthy and actionable insights to guide personalized cancer care.