Pulmonary nodule detection in CT scans with equivariant CNNs

Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain for medical imaging problems. In this work we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions instead of standard translational convolutions. 3D CNNs with group convolutions (3D G-CNNs) were applied to the problem of false positive reduction for pulmonary nodule detection in CT scans, and proved to be substantially more effective in terms of accuracy, sensitivity to malignant nodules, and speed of convergence compared to a strong and comparable baseline architecture with regular convolutions, extensive data augmentation and a similar number of parameters. For every dataset size tested, the G-CNN achieved a FROC score close to the CNN trained on ten times more data.