[2N3-J-13] AI application: medical diagnosis

[2N3-J-13-02] Character-level Text Generations with Attention for Chest X-ray Diagnosis

〇Kenya Sakka¹, Kotaro Nakayama^1,2, Nisei Kimura¹, Taiki Inoue¹, Ryohei Yamaguchi¹, Yoshimasa Kawazoe¹, Kazuhiko Ohe¹, Yutaka Matsuo¹ (1. The University of Tokyo, 2. NABLAS Inc.)

Keywords:medical image, character level, attention mechanism, case generation

Medical images are widely used in clinical practice for diagnosis and treatment, and much time is spent on diagnosis. Therefore, research to automatically generate cases from medical images has been actively conducted in recent years. However, it is difficult to judge the case as a classification problem because there are orthographic variants in the case written in the medical certificate.
In this paper, we aimed to automatically generate character-level cases in order to cope with orthographic variants on chest X-ray images. In addition, the interpretability of the result was improved by introducing an attention mechanism. As a result, it was confirmed cases with features such as position information were generated, and the effectiveness of character-level approach was shown in text generation of medical images.