NoduLoCC2026: Lung Nodule Localization and Classification Contest from Chest X-Ray Images

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.

Adnan Mustafic, Halim Benhabiles, Adnane Cabani, Kristhian André Oliveira Aguilar, Romain Amigon, Clément Bardin, Chiara Bentifece, Marin Boehm, Kévin Bouchard, Laura Burattini, Diedre Carmo, Fahima Idiri, Matthis Lahargoue, Ilaria Marcantoni, Hicham Messaoudi, Cyril Meyer, Farid Meziane, Léon Morales, Letícia Rittner, Agnese Sbrollini, Léonard Zipper, Karim Hammoudi · 2026