Abstract
Chest radiography is a general method for diagnosing a patient’s condition and identifying important information. Therefore, a large amount of chest radiographs have been taken. In order to reduce the burden on medical professionals, methods for generating findings have been proposed. However, the study of generating chest radiograph findings has primarily focused on the English language, and to the best of our knowledge, no studies have studied Japanese data on this subject. The difficult points of the Japanese language are that the boundaries of words are not clear and that there are numerous orthographic variants. For deal with two problems, we proposed an end-to-end attention-based model that generates Japanese findings at the character-level from chest radiographs. We evaluated the method using a public dataset of Japanese chest radiograph findings. Furthermore, we confirmed via visual inspection that the attention mechanism captures the features and positional information of radiographs.
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Acknowledgements
This research was supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research JP25700032, JP15H05327, JP16H06562 and Japan Agency for Medical Research and Development (AMED) of ICT infrastructure construction research business such as clinical research in 2016.
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Sakka, K. et al. (2021). Character-Level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis. In: Shaban-Nejad, A., Michalowski, M., Buckeridge, D.L. (eds) Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_13
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DOI: https://doi.org/10.1007/978-3-030-53352-6_13
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