No New-Net

Understand

In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge.

• This endeavour is particularly interesting given that researchers are currently besting each other with architectural modifications that are intended to improve the segmentation performance.
• We instead focus on the training process arguing that a well trained U-Net is hard to beat.
• Our baseline U-Net, which has only minor modifications and is trained with a large patch size and a Dice loss function indeed achieved competitive Dice scores on the BraTS2018 validation data.