Handcrafted features can boost performance and data-efficiency for deep detection of lung nodules from CT imaging

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