||Deep-Neural-Network based image diagnosis: comparing various image preprocessing
Tachibana, Yasuhiko ,
Obata, Takayuki ,
Kershaw, Jeffrey ,
Ikoma, Yoko ,
Kishimoto, RiwaHigashi, Tatsuya
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