Poster: Attempts on detecting Alzheimer's disease by fine-tuning pre-trained model with Gaze Data

被引：0

作者：

Nagasawa, Junichi ^{[1
,2
]}

Nakata, Yuichi ^{[1
,2
]}

Hiroe, Mamoru ^{[1
,3
]}

Zheng, Yujia ^{[1
]}

Kawaguchi, Yutaka ^{[1
]}

Maegawa, Yuji ^{[1
]}

Hojo, Naoki ^{[1
]}

Takiguchi, Tetsuya ^{[1
]}

Nakayama, Minoru ^{[4
]}

Uchimura, Maki ^{[1
]}

Sonoda, Yuma ^{[1
]}

Kowa, Hisatomo ^{[1
]}

Nagamatsu, Takashi ^{[1
]}

机构：

[1] Kobe Univ, Kobe, Hyogo, Japan

[2] Kwansei Gakuin Univ, Sanda, Japan

[3] Osaka Seikei Univ, Osaka, Japan

[4] Tokyo Inst Technol, Tokyo, Japan

来源：

PROCEEDINGS OF THE 2024 ACM SYMPOSIUM ON EYE TRACKING RESEARCH & APPLICATIONS, ETRA 2024 | 2024年

关键词：

Alzheimer's disease; Antisaccade; Eye movement classifier; Fine-tuning;

D O I：

10.1145/3649902.3655650

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This poster presents a study on detecting Alzheimer's disease (AD) using deep learning from gaze data. In this study, we modify an existing pre-trained deep neural network model, gazeNet, for transfer learning. The results suggest the possibility of applying this method to mild cognitive impairment screening tests.

引用

页数：3

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