Fig. 3. Performance on unseen radiographic findings in the PadChest dataset.
Mean AUC and 95% CI are shown for each radiographic finding (n > 50) labelled as high importance by an expert radiologist. We externally validated the model’s ability to generalize to different data distributions by evaluating model performance on the human-annotated subset of the PadChest dataset (n = 39,053 chest X-rays). No labelled samples were seen during training for any of the radiographic findings in this dataset. The self-supervised method achieves an AUC of at least 0.900 on 6 findings and at least 0.700 on 38 findings out of 57 radiographic findings where n > 50 in the PadChest test dataset (n = 39,053).