Automatic Lung Cancer Follow-Up Recommendation with 3D Deep Learning

Abstract

Lung cancer is the most common form of cancer in the world affecting millions yearly. Early detection and treatment is critical in driving down mortality rates for this disease. A traditional form of early detection involves radiologists manually screening low dose computed tomography scans which can be tedious and time consuming. We propose an automatic system of deep learning methods for the detection, segmentation, and classification of pulmonary nodules. The system is composed of 3D convolutional neural networks based on VGG and U-Net architectures. Chest scans are received as input and, through a series of patch-wise predictions, patient follow-up recommendations are predicted based on the 2017 Fleischner society pulmonary nodule guidelines. The system was developed as part of the LNDb challenge and participated in the main challenge as well as all sub-challenges. While the proposed method struggled with false positives for the detection task and a class imbalance for the texture characterization task, it presents a baseline for future work.