Radiomics Feature

Radiomics involves extracting quantitative features from medical images to improve disease diagnosis and treatment planning. Current research focuses on developing robust feature extraction methods, often employing deep learning architectures like diffusion models, Vision Transformers, and convolutional neural networks, to address challenges like inter-scanner variability and incomplete data. These advancements aim to enhance the accuracy and reliability of radiomics-based predictive models for various cancers and neurological diseases, ultimately improving clinical decision-making and patient care. The field is also actively exploring methods to improve model interpretability and address potential biases in data.