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Deep learning has shown great potential in assisting radiologists in readingchest X-ray (CXR) images, but its need for expensive annotations for improvingperformance prevents widespread clinical application. Visual languagepre-training (VLP) can alleviate the burden and cost of annotation byleveraging routinely generated reports for radiographs, which exist in largequantities as well as in paired form (image-text pairs). Additionally,extensions to localization-aware VLPs are being proposed to address the needsfor accurate localization of abnormalities for computer-aided diagnosis (CAD)in CXR. However, we find that the formulation proposed by locality-aware VLPliterature actually leads to a loss in spatial relationships required fordownstream localization tasks. Therefore, we propose Empowering Locality of VLPwith Intra-modal Similarity, ELVIS, a VLP aware of intra-modal locality, tobetter preserve the locality within radiographs or reports, which enhances theability to comprehend location references in text reports. Our locality-awareVLP method significantly outperforms state-of-the art baselines in multiplesegmentation tasks and the MS-CXR phrase grounding task. Qualitatively, we showthat ELVIS focuses well on regions of interest described in the report textcompared to prior approaches, allowing for enhanced interpretability.

ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity