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Chest radiography is a general method for diagnosing a patient's conditionand identifying important information; therefore, radiography is usedextensively in routine medical practice in various situations, such asemergency medical care and medical checkup. However, a high level of expertiseis required to interpret chest radiographs. Thus, medical specialists spendconsiderable time in diagnosing such huge numbers of radiographs. In order tosolve these problems, methods for generating findings have been proposed.However, the study of generating chest radiograph findings has primarilyfocused on the English language, and to the best of our knowledge, no studieshave studied Japanese data on this subject. There are two challenges involvedin generating findings in the Japanese language. The first challenge is thatword splitting is difficult because the boundaries of Japanese word are notclear. The second challenge is that there are numerous orthographic variants.For deal with these two challenges, we proposed an end-to-end model thatgenerates Japanese findings at the character-level from chest radiographs. Inaddition, we introduced the attention mechanism to improve not only theaccuracy, but also the interpretation ability of the results. We evaluated theproposed method using a public dataset with Japanese findings. Theeffectiveness of the proposed method was confirmed using the BilingualEvaluation Understudy score. And, we were confirmed from the generated findingsthat the proposed method was able to consider the orthographic variants.Furthermore, we confirmed via visual inspection that the attention mechanismcaptures the features and positional information of radiographs.

Character-level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis