Presentation Deep-Neural-Network based image diagnosis: comparing various image preprocessing

Tachibana, Yasuhiko  ,  Obata, Takayuki  ,  Kershaw, Jeffrey  ,  Ikoma, Yoko  ,  Kishimoto, Riwa  ,  Higashi, 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

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