3 0 0 0 OA MIRU2014若手プログラム実施報告と次回の企画紹介
- 著者
- 島田 敬士 浦西 友樹 上瀧 剛 柴田 剛志 道満 恵介 豊浦 正広 柳川 由紀子
- 出版者
- 一般社団法人電子情報通信学会
- 雑誌
- 情報・システムソサイエティ誌 (ISSN:21899797)
- 巻号頁・発行日
- vol.20, no.4, pp.21-24, 2016 (Released:2016-05-25)
- 参考文献数
- 2
- 被引用文献数
- 1 1
1 0 0 0 一拍脈波特徴量を用いた血圧脈波のフェーズ推定法
1 0 0 0 耐ノイズ性を高めた血圧計測法
1 0 0 0 OA 顔色分析によるパーソナルカラー診断システムの検討
- 著者
- 柳川 由紀子
- 出版者
- 奈良先端科学技術大学院大学
- 巻号頁・発行日
- 2004-03-24
- 著者
- 柳川 由紀子 越後 富夫 宮崎 祐太 武村 紀子 八木 康史
- 出版者
- 一般社団法人 人工知能学会
- 雑誌
- 人工知能学会論文誌 (ISSN:13460714)
- 巻号頁・発行日
- vol.33, no.6, pp.C-I33_1-12, 2018-11-01 (Released:2018-11-01)
- 参考文献数
- 51
Tracking precisely of abnormalities in the gastrointestinal tract is useful for preparing sample image sequences on educational training for medical diagnose on endoscopy. While the gastrointestinal wall deforms continuously in an unpredictable manner, however, abnormalities without distinctive features make it difficult to track over continuous frames. To address this problem, the proposed method employs Convolutional neural networks (CNN) for tracking lesion area. Conventionally, CNN for tracking requires a large amount of sample data for preliminary learning. The state-of-arts tracking methods using CNN are premised on preliminary learning on data similar to target images given a large number of correct answer labels. On the other hand, the proposed method are not required preliminary learning using similar data. The image components in the marked region at the starting frame is similar to components at the only same position, but different between them depending on the degree of overlapped area. Furthermore, in the successive frame, the components in the previous region is similar to them in the identified area. Therefore, similarity can be learned in the previous frame, called it as an intra-frame training. This paper describes the method for tracking an abnormal region by using CNN based on training overlap rates between the abnormal region and local scanning one with the same size on the starting intra-frame. Furthermore, network parameters are transformed from training the similar regions on the continuous frame additionally. We demonstrate the efficiency of the proposed approach using eight common types of gastrointestinal abnormality.