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Convolutional neural networks (CNNs) have been successfully applied to chestx-ray (CXR) images. Moreover, annotated bounding boxes have been shown toimprove the interpretability of a CNN in terms of localizing abnormalities.However, only a few relatively small CXR datasets containing bounding boxes areavailable, and collecting them is very costly. Opportunely, eye-tracking (ET)data can be collected in a non-intrusive way during the clinical workflow of aradiologist. We use ET data recorded from radiologists while dictating CXRreports to train CNNs. We extract snippets from the ET data by associating themwith the dictation of keywords and use them to supervise the localization ofspecific abnormalities. We show that this method improves a model'sinterpretability without impacting its image-level classification.

Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation