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Quantifying the uncertainty of neural networks using Monte Carlo dropout for deep learning based quantitative MRI

2021-12-02 20:04:40
Mehmet Yigit Avci, Ziyu Li, Qiuyun Fan, Susie Huang, Berkin Bilgic, Qiyuan Tian
arXiv_AI
               
Abstract

Dropout is conventionally used during the training phase as regularization method and for quantifying uncertainty in deep learning. We propose to use dropout during training as well as inference steps, and average multiple predictions to improve the accuracy, while reducing and quantifying the uncertainty. The results are evaluated for fractional anisotropy (FA) and mean diffusivity (MD) maps which are obtained from only 3 direction scans. With our method, accuracy can be improved significantly compared to network outputs without dropout, especially when the training dataset is small. Moreover, confidence maps are generated which may aid in diagnosis of unseen pathology or artifacts.

Abstract (translated)

URL

https://arxiv.org/abs/2112.01587

PDF

https://arxiv.org/pdf/2112.01587.pdf

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