RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
Clinically significant prostate cancer (csPCa) is a leading cause of cancer
death in men, yet it has a high survival rate if diagnosed early. Bi-parametric
MRI (bpMRI) reading has become a prominent screening test for csPCa. However,
this process has a high false positive (FP) rate, incurring higher diagnostic
costs and patient discomfort. This paper introduces RadHop-Net, a novel and
lightweight CNN for FP reduction. The pipeline consists of two stages: Stage 1
employs data driven radiomics to extract candidate ROIs. In contrast, Stage 2
expands the receptive field about each ROI using RadHop-Net to compensate for
the predicted error from Stage 1. Moreover, a novel loss function for
regression problems is introduced to balance the influence between FPs and true
positives (TPs). RadHop-Net is trained in a radiomics-to-error manner, thus
decoupling from the common voxel-to-label approach. The proposed Stage 2
improves the average precision (AP) in lesion detection from 0.407 to 0.468 in
the publicly available pi-cai dataset, also maintaining a significantly smaller
model size than the state-of-the-art.
