e-Article
Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization.
Document Type
Article
Author
Source
Subject
*EARLY detection of cancer
*PROSTATE cancer
*PROSTATE cancer patients
*PROSTATE
*CANCER diagnosis
*SPACE
*DEEP learning
*CUBES
*
*
*
*
*
*
*
Language
ISSN
2045-2322
Abstract
Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians. [ABSTRACT FROM AUTHOR]