Lung Nodule Malignancy

Lung nodule malignancy prediction aims to accurately differentiate benign from malignant lung nodules, improving early cancer detection and treatment planning. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, transformers, and ensemble methods, often incorporating techniques like transfer learning and contrastive learning to enhance accuracy and robustness. These advancements focus on improving diagnostic accuracy through better feature extraction from CT scans, handling noisy or incomplete labels, and incorporating contextual information to mimic radiologist decision-making processes. Ultimately, improved prediction models hold significant potential to reduce mortality rates and improve patient outcomes by enabling earlier and more precise interventions.