  Pulmonary nodules may be an early manifestation of lung cancer, the leading
cause of cancer-related deaths among both men and women. Numerous studies have
established that deep learning methods can yield high-performance levels in the
detection of lung nodules in chest X-rays. However, the lack of gold-standard
public datasets slows down the progression of the research and prevents
benchmarking of methods for this task. To address this, we organized a public
research challenge, NODE21, aimed at the detection and generation of lung
nodules in chest X-rays. While the detection track assesses state-of-the-art
nodule detection systems, the generation track determines the utility of nodule
generation algorithms to augment training data and hence improve the
performance of the detection systems. This paper summarizes the results of the
NODE21 challenge and performs extensive additional experiments to examine the
impact of the synthetically generated nodule training images on the detection
algorithm performance.
