Document detail
ID
oai:arXiv.org:2501.02066
Topic
Computer Science - Computer Vision... Electrical Engineering and Systems...Author
Magoulianitis, Vasileios Yang, Jiaxin Alexander, Catherine A. Kuo, C. -C. JayCategory
Computer Science
Year
2025
listing date
1/8/2025
Keywords
cancer stageAbstract
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.
;Comment: 5 pages, 4 figures - Accepted to IEEE International Symposium on Biomedical Imaging (ISBI 2025)
Magoulianitis, Vasileios,Yang, Jiaxin,Alexander, Catherine A.,Kuo, C. -C. Jay, 2025, RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection
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