3D Nodule Content-Based Metric Learning for Evidence-Based Lung Cancer Screening

The characteristics of 3D nodules on Computed Tomography (CT), including size, location, shape, and attenuation, are primary medical clues for distinguishing between benign and malignant nodules. To support evidence-based decision-making for lung cancer screening in clinical practice, we present a 3D Nodule Content-based Metric Learning (3D-NCML) network to retrieve subsolid-benign, subsolid-malignant, solid-benign, and solid-malignant nodules similar to the indeterminate ones. The inputs of 3D-NCML are 3D patches that exactly contain the whole nodule to ensure all visual information is included. A spatial position and size coding module, a shape encoder module, and an attenuation extraction module are designed based on medical clues for guiding the network to learn important characteristics of nodules. Experiments on the LIDC-IDRI dataset and a private dataset demonstrate that 3D-NCML outperforms other methods by quantitative and qualitative analysis, with more similar nodules retrieved and ranked ahead.