Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images.

ABSTRACT:

Background

Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI)

SUBMITTER: Sobecki P 

PROVIDER: S-EPMC7953869 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature