Are X-ray landmark detection models fair? A preliminary assessment and mitigation strategy

Conference item

Datasets used for benchmarking are always acquired with a view to representing different categories equally, with the best intentions to be fair to all. Whilst it is usually assumed that equal numerical representation in the training data leads to similar accuracy among demographic groups, so far, there has been next to no investigation or measurement of this assumption for the anatomical landmark detection task. In this work, we define what it means for anatomical landmark detection to be carried out fairly on different demographic categories, evaluating the fairness of models trained on two publicly available X-ray datasets that are known to be balanced, and showing how unfair predictions can uncover metadata attributes intended to be hidden. We further design a potential mitigation strategy in the landmark detection context, adapting a group optimization method typically employed for debiasing image classification models, obtaining a partial improvement in terms of per-keypoint fairness, while paving the way for further research in this field.

Authors: Di Via, R; Ciranni, M; Marinelli, D; Clement, A; Patel, N

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: ICCV 2025 Workshop Proceedings
• Publication date: 2025-10-19
• Acceptance date: 2025-07-22
• Event title: STREAM Workshop (Systematic Trust in AI Models: Ensuring Fairness, Reliability, Explainability, and Accountability in Machine Learning Frameworks) @ ICCV 2025
• Event location: Honolulu, Hawaii
• Event website: https://stream-workshop.github.io/stream-workshop/
• Event start date: 2025-10-19
• Event end date: 2025-10-19
• Language: English