Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization

TL;DR

This paper presents an auto-Deep Learning approach for detecting and localizing prostate cancer from mpMRI, using separate models for different prostate zones to improve clinical interpretability and efficiency.

Contribution

It introduces a novel auto-Deep Learning pipeline with zone-specific models trained on 2.5D slices, advancing automatic prostate cancer diagnosis towards clinical application.

Findings

Effective detection of suspicious slices in mpMRI

Zone-specific models improve interpretability

Automated approach reduces physician workload

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 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.