3D G-CNNs for Pulmonary Nodule Detection

TL;DR

This paper demonstrates that 3D roto-translation group convolutions significantly improve CNN performance and data efficiency in pulmonary nodule detection, reducing false positives and enhancing sensitivity with fewer training samples.

Contribution

The introduction of 3D G-Convs for pulmonary nodule detection improves CNN data efficiency and performance over traditional convolutions, especially with limited training data.

Findings

G-Convs outperform regular convolutions in FROC scores

G-Convs require less training data for similar performance

Faster convergence with G-Convs in pulmonary nodule detection

Abstract

Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Convs) instead of the more conventional translational convolutions. These 3D G-CNNs were applied to the problem of false positive reduction for pulmonary nodule detection, and proved to be substantially more effective in terms of performance, sensitivity to malignant nodules, and speed of convergence compared to a strong and comparable baseline architecture with regular convolutions, 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.