肺结节检测与定位竞赛，展现分类效果好但定位仍难。
NoduLoCC2026: Lung Nodule Localization and Classification Contest from Chest X-Ray Images
- 组织国际团队开展胸片中肺结节的检测与定位挑战赛。
- 最佳模型分类准确率72%，但定位正确率仅53%，平均偏差12.83mm。
- 适合医学影像分析与临床辅助诊断研究者参考。
我们提出NoduLoCC2026，一项针对胸部X光片中肺结节检测与定位的竞赛。我们提供了用于两项任务的数据集，并收到5支国际团队的参赛方案。本文展示了各团队方法及其在外部测试数据集上的结果。分类任务表现良好，最优方法平衡准确率达0.72，AUC-ROC为0.79。定位任务则存在明显局限：最佳方法仅在53%的测试图像上预测出正确的结节数量，中位距离为12.83mm，表明该任务更具挑战性。竞赛官网可访问：https://gt-i2mdp.github.io/website/nodule_challenge.html。
原文摘要 · Abstract (English)
We propose NoduLoCC2026, a challenge on lung nodule detection and localization in chest X-ray images. We have provided a dataset for both tasks and received submissions from 5 international teams. The participating teams' solutions are presented in this work along with results on an external dataset used for testing. Proposed methods show good performance on the classification task. The best method shows a balanced accuracy score of 0.72 and AUC-ROC of 0.79. We highlight the limitations of current approaches for the localization task, with the best approach having predicted the correct number of nodules on 53\% of the test images with a median distance of 12.83mm, showing that it is a more challenging task than the first one. The challenge website is available via https://gt-i2mdp.github.io/website/nodule_challenge.html.
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