Department(s)
Natural Science
Document Type
Article
Version Deposited
Published version
Publication Date
2024
Abstract
Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the model’s detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.
Publication Title
Scientific Reports
E-ISSN
20452322
Volume
14
Issue
1
DOI
10.1038/s41598-024-56405-7
PubMed ID
38459100
Recommended Citation
Zheng, H., Hung, A.L.Y., Miao, Q. et al. AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI. Sci Rep 14, 5740 (2024). https://doi.org/10.1038/s41598-024-56405-7
Comments
Publication can be accessed at this link: https://10.1038/s41598-024-56405-7