@misc{oai:repo.qst.go.jp:00066661,
 author = {Tachibana, Yasuhiko and Obata, Takayuki and Kershaw, Jeffrey and Ikoma, Yoko and Kishimoto, Riwa and Higashi, Tatsuya and 立花 泰彦 and 小畠 隆行 and Ｋｅｒｓｈａｗ Ｊｅｆｆｒｅｙ and 生駒 洋子 and 岸本 理和 and 東 達也},
 month = {Apr},
 note = {The purpose of this study was to investigate how image preprocessing might help overcome two problems for deep-neural-network (DNN) based image diagnosis: the need for a large training database to achieve high accuracy and the difficulty humans have in understanding the internal decision process. Five DNNs were trained with a brain image series (preprocessed in five different ways), to judge the age-range of a volunteer. The performance of the DNNs was then compared statistically. The results suggested that image preprocessing may facilitate higher accuracy, and also make it easier to understand how and why a judgement was made., ISMRM 2016 2016 Annual Meeting},
 title = {Deep-Neural-Network based image diagnosis: comparing various image preprocessing},
 year = {2017}
}