Handcrafted Features Can Boost Performance and Data-Efficiency for Deep Detection of Lung Nodules From CT Imaging

Abstract

Convolutional neural networks have been widely used to detect and classify various objects and structures in computer vision and medical imaging. Access to large sets of annotated data is commonly a prerequisite for achieving good performance. Before the deep learning era, systems based on handcrafted features were employed, which typically required less annotated data but also reached inferior performance. In this work, we investigate the benefit of combining deep learning using a convolutional neural network (CNN), with handcrafted features for lung nodule detection from CT imaging. We investigate three fusion strategies with increasing complexity, and evaluate their performance for varying amounts of training data. Our results indicate that combining handcrafted features with a 3D CNN approach significantly improves lung nodule detection performance in comparison to an independently trained CNN model, regardless of the fusion strategy. Comparatively larger increases in performance were obtained when less training data was available. The fusion strategy in which features are combined with a CNN using a single end-to-end training scheme performed best overall, allowing to reduce training data by 33% to 43%, while maintaining performance. Among the investigated handcrafted features, those that describe the relative position of the candidate with respect to the lung wall and mediastinum, were found to be of most benefit.

Keywords

• 3D CNN
• convolutional neural networks
• deep learning
• data augmentation
• false positive reduction
• handcrafted features