Lung Lesion

Lung lesion analysis focuses on accurately identifying and characterizing abnormalities in lung tissue, primarily aiding in the diagnosis and treatment of diseases like lung cancer and cystic fibrosis. Current research emphasizes automated lesion segmentation and detection using various deep learning architectures, including 2D and 3D convolutional neural networks (CNNs), variational autoencoders (VAEs), and novel approaches incorporating semi-supervised learning and online interactive segmentation with scribbles. These advancements aim to improve diagnostic accuracy, reduce the time and effort required for manual analysis, and enable more robust radiomic modeling for prognosis and treatment planning. The ultimate goal is to improve patient care through faster, more accurate, and less labor-intensive diagnostic workflows.