2025-09-18 愛媛大学
ViFTのモデル構造
<関連情報>
- https://www.ehime-u.ac.jp/data_relese/pr_20250918_media/
- https://www.ehime-u.ac.jp/wp-content/uploads/2025/09/pr_20250918_media.pdf
- https://www.sciencedirect.com/science/article/pii/S1361841525002683
深層強化学習による視野検査のための視野トランスフォーマ ViFT: Visual field transformer for visual field testing via deep reinforcement learning
Shozo Saeki, Minoru Kawahara, Hirohisa Aman
Medical Image Analysis
DOI:https://doi.org/10.1016/j.media.2025.103721
Highlights
- ViFT single agent can control the perimetry process fully.
- ViFT learns the relationships of visual field locations without any pre-defined information.
- ViFT learning process can consider the patient perception uncertainty.
- ViFT can measure half as measurement times as the other strategies maintain high accuracy.
Abstract
Visual field testing (perimetry) quantifies a patient’s visual field sensitivity to diagnosis and follow-up on their visual impairments. Visual field testing would require the patients to concentrate on the test for a long time. However, a longer testing time makes patients more exhausted and leads to a decrease in testing accuracy. Thus, it is helpful to develop a well-designed strategy to finish the testing more quickly while maintaining high accuracy. This paper proposes the visual field transformer (ViFT) for visual field testing with deep reinforcement learning. This study contributes to the following four: (1) ViFT can fully control the visual field testing process. (2) ViFT learns the relationships of visual field locations without any pre-defined information. (3) ViFT learning process can consider the patient perception uncertainty. (4) ViFT achieves the same or higher accuracy than the other strategies, and half as test time as the other strategies. Our experiments demonstrate the ViFT efficiency on the 24-2 test pattern compared with other strategies.
