On the impact of greyscale imagenet pre-training for chest x-ray model transferability.

Abstract

Standard ImageNet pre-training relies on RGB natural images, introducing visual features such as colour and texture that may misalign with medical imaging modalities, such as chest X-rays (CXRs). In this work, we conduct a systematic analysis of greyscale ImageNet pre-training, using both three-channel and single-channel model variants, evaluating their perturbation stability, attribution alignment, and transferability. First, we train and benchmark three ResNet-50 backbones (RGB, 3c-Grey, 1c-Grey) on ImageNet-1K, using the model-vs-human framework, and find that greyscale variants improve top-1 accuracy under parametric and binary image perturbations by up to 10.9%, with average gains of 4.23–4.43% over RGB. Then, using these backbones, we transfer-learn to a CXR nodule classification task, and evaluate model generalisation across four public datasets. Greyscale variants, particularly the single-channel model, achieve up to 3.5% higher F1 scores, with average gains of 1–3% over RGB. Finally, we perform a quantified attribution analysis that reveals that greyscale models produce saliency maps with stronger alignment to expert-annotated nodules, yielding 5% higher nodule coverage and 1.7% higher IoU on average. We release our greyscale pre-trained weights to support further work on generalisable and shortcut-resistant medical imaging. https://github.com/sophie-haynes/greyscale-imagenet-for-cxr