  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 where we trained a customized convolutional neural network on a
public cohort with 201 patients and the cropped 2D patches around the region of
interest were used as the input, 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. Something different was peripheral zone (PZ) and central gland
(CG) were trained and tested separately, the PZ detector and CG detector were
demonstrated effectively in highlighting the most suspicious slices out of a
sequence, hopefully to greatly ease the workload for the physicians.
