Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores. (arXiv:2308.04653v1 [eess.IV])

This study focuses on comparing deep learning methods for the segmentation
and quantification of uncertainty in prostate segmentation from MRI images. The
aim is to improve the workflow of prostate cancer detection and diagnosis.
Seven different U-Net-based architectures, augmented with Monte-Carlo dropout,
are evaluated for automatic segmentation of the central zone, peripheral zone,
transition zone, and tumor, with uncertainty estimation. The top-performing
model in this study is the Attention R2U-Net, achieving a mean Intersection
over Union (IoU) of 76.3% and …

aim architectures arxiv cancer cancer detection deep learning detection diagnosis dropout images monte-carlo mri performance quantification segmentation study uncertainty workflow