đ€ AI Summary
This study addresses the insufficient accuracy in detecting and localizing pulmonary nodules in chest X-ray images by organizing an international challenge that systematically evaluates diverse deep learning approaches on a unified real-world dataset. The assessment encompasses object detection models, classification architectures, and post-processing strategies, revealing inherent difficulties in nodule localization. Results demonstrate that the best-performing classification method achieves a balanced accuracy of 0.72 and an AUC-ROC of 0.79. However, for localization, only 53% of images yield correct predictions of nodule count, with a median distance error of 12.83 mm, underscoring the current limitations of existing methods in achieving precise spatial localization.
đ Abstract
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.