@inproceedings{discovery10200673,
         journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
           title = {Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms},
            year = {2024},
       publisher = {Springer, Cham},
           month = {October},
          series = {Lecture Notes in Computer Science, volume 15198},
          editor = {Esther Puyol-Ant{\'o}n and Ghada Zamzmi and Aasa Feragen and Andrew P King and Veronika Cheplygina and Melanie Ganz-Benjaminsen and Enzo Ferrante and Ben Glocker and Eike Petersen and John SH Baxter and Islem Rekik and Roy Eagleson},
       booktitle = {Ethics and Fairness in Medical Imaging: FAIMI EPIMI 2024},
           pages = {78--87},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          volume = {15198},
             url = {http://dx.doi.org/10.1007/978-3-031-72787-0\%5f8},
          author = {McCabe, J and Cheng, D and Bhamani, A and Mullin, M and Patrick, T and Nair, A and Janes, SM and Sudre, CH and Jacob, J},
            issn = {0302-9743},
        abstract = {Lung cancer is the leading cause of cancer mortality worldwide. Asymptomatic in its early stages, it is disproportionately detected when the disease is advanced. Resource constraints have resulted in increasing reliance on computer-aided detection (CADe) systems to assist with scan evaluation. The datasets used to train these algorithms are often unbalanced in their representation of protected groups e.g. sex and ethnicity. This project investigates whether there are performance disparities in detecting clinically relevant nodules across under-represented groups in selected, state-of-the-art nodule detection algorithms trained on data from a screening program in the UK. Our analysis revealed that overall, the algorithms demonstrate equitable performance across various demographic groups. However, their performance varies strongly across nodule characteristics (size and type) in line with their prevalence in the training set. To ensure continued equitable performance, algorithms should not only consider demographic but also nodule attributes representativeness in their training.},
        keywords = {Nodule Detection Algorithms, Fairness in AI, Lung Cancer
Screening}
}