[2603.05157] The Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.05157 (cs) [Submitted on 5 Mar 2026] Title:The Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis Authors:Dishantkumar Sutariya, Eike Petersen View a PDF of the paper titled The Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis, by Dishantkumar Sutariya and 1 other authors View PDF HTML (experimental) Abstract:Deep learning models can identify racial identity with high accuracy from chest X-ray (CXR) recordings. Thus, there is widespread concern about the potential for racial shortcut learning, where a model inadvertently learns to systematically bias its diagnostic predictions as a function of racial identity. Such racial biases threaten healthcare equity and model reliability, as models may systematically misdiagnose certain demographic groups. Since racial shortcuts are diffuse - non-localized and distributed throughout the whole CXR recording - image preprocessing methods may influence racial shortcut learning, yet the potential of such methods for reducing biases remains underexplored. Here, we investigate the effects of image preprocessing methods including lung masking, lung cropping, and Contrast Limited Adaptive Histogram Equalization (CLAHE). These approaches aim to suppress spurious cues encoding racial information while preserving diagnostic accuracy. Our experiments reveal that simple bounding box-based lung croppin...