Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with un...

Full description

Bibliographic Details
Main Authors: Yongkai Liu, Guang Yang, Melina Hosseiny, Afshin Azadikhah, Sohrab Afshari Mirak, Qi Miao, Steven S. Raman, Kyunghyun Sung
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Prostate zones
automatic segmentation
Bayesian deep learning
attentive modules
Online Access:https://ieeexplore.ieee.org/document/9169868/

Internet

https://ieeexplore.ieee.org/document/9169868/

Similar Items