Nodule detection and generation on chest X-rays: NODE21 Challenge

TL;DR

The NODE21 challenge advances lung nodule detection research by providing a public dataset and benchmarking detection and generation algorithms, demonstrating how synthetic images can enhance detection performance.

Contribution

This paper introduces the NODE21 challenge, offering a new dataset and benchmarks for lung nodule detection and generation, and evaluates the impact of synthetic images on detection accuracy.

Findings

Synthetic nodule images improve detection performance.

Benchmarking reveals strengths and weaknesses of current methods.

Generation algorithms can augment training data effectively.

Abstract

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.