Abstract
The COVID-19 pandemic continues to spread and impact the well-being of the global population. The front-line imaging modalities computed tomography (CT) and X-ray play an important role for triaging COVID-19 patients. Considering the limited access to resources (both hardware and trained personnel) and decontamination, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based applications for triaging and monitoring COVID-19 patients in a timely manner with the additional ability to delineate the disease region boundary are seen as a promising solution. Our proposed solution differs from existing solutions by industry and academic communities. We demonstrates a functional AI model to triage by inferencing using a single x-ray image, while the AI model is trained using both X-ray and CT data. We report on how such a multi-modal training improves the solution compared to X-ray only training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 for the classification between COVID-19 and non-COVID-19 cases. It also positively impacts the Dice coefficient (0.59 to 0.62) for segmenting the COVID-19 pathology.
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Tan, T. et al. (2021). Pristine Annotations-Based Multi-modal Trained Artificial Intelligence Solution to Triage Chest X-Ray for COVID-19. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_31
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DOI: https://doi.org/10.1007/978-3-030-87234-2_31
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