IEEE Trans Med Imaging. 2024 Aug;43(8):2839-2853. doi: 10.1109/TMI.2024.3382042. Epub 2024 Aug 1.
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.
肺部结节可能是肺癌的早期表现,也是男性和女性癌症相关死亡的主要原因。许多研究已经证实,深度学习方法可以在胸部 X 光片中检测肺结节方面达到高性能水平。然而,缺乏黄金标准的公共数据集减缓了研究的进展,并阻止了针对该任务的方法的基准测试。为了解决这个问题,我们组织了一个名为 NODE21 的公共研究挑战,旨在检测和生成胸部 X 光片中的肺结节。虽然检测跟踪评估了最先进的结节检测系统,但生成跟踪确定了结节生成算法在增强训练数据方面的实用性,从而提高了检测系统的性能。本文总结了 NODE21 挑战赛的结果,并进行了广泛的额外实验,以检查合成生成的结节训练图像对检测算法性能的影响。