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Diagnostic Classification of Chest X-Rays Pictures with Deep Learning Using Eye Gaze Data
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- INOUE Taiki
- Graduate School of Pharmaceutical Sciences, The University of Tokyo
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- KIMURA Nisei
- Graduate School of Engineering, The University of Tokyo
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- KOTARO Nakayama
- Graduate School of Engineering, The University of Tokyo NABLAS Inc.
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- SAKKA Kenya
- Graduate School of Frontier Sciences, The University of Tokyo
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- RAHMAN Abdul Ghani Abdul
- Graduate School of Engineering, The University of Tokyo
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- NAKAJIMA Ai
- Aalto University
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- PATRICK Radkohl
- Graduate School of Engineering, The University of Tokyo
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- IWAI Satoshi
- Graduate School of Medicine, The University of Tokyo
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- KAWAZOE Yoshimasa
- Graduate School of Medicine, The University of Tokyo
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- OHE Kazuhiko
- Graduate School of Medicine, The University of Tokyo
Bibliographic Information
- Other Title
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- 視線データを活用した深層学習による胸部 X 線写真の診断的分類
Description
<p>Automatic diagnosis of chest X-ray pictures with deep learning has been extensively studied in recent years. In order to improve the accuracy, it is important how to input small localized areas which are disease specific while at the same time using the information that can be obtained by the whole picture. We considered that human eye-gaze fixations can be a biomarker that indicates areas specific to disease. In this study, we propose a deep learning model utilizing eye-gaze data. We demonstrate that the classification shows the better accuracy on using eye-gaze data of experienced doctors than eye-gaze data of novice or non-use of eye-gaze information.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2019 (0), 1H3J1302-1H3J1302, 2019
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390282763118485888
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- NII Article ID
- 130007658270
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed