Hybrid-View Attention for csPCa Classification in TRUS
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Prostate cancer (PCa) is a leading cause of cancer-related mortality in men,
and accurate identification of clinically significant PCa (csPCa) is critical
for timely intervention. Transrectal ultrasound (TRUS) is widely used for
prostate biopsy; however, its low contrast and anisotropic spatial resolution
pose diagnostic challenges. To address these limitations, we propose a novel
hybrid-view attention (HVA) network for csPCa classification in 3D TRUS that
leverages complementary information from transverse and sagittal views. Our
approach integrates a CNN-transformer hybrid architecture, where convolutional
layers extract fine-grained local features and transformer-based HVA models
global dependencies. Specifically, the HVA comprises intra-view attention to
refine features within a single view and cross-view attention to incorporate
complementary information across views. Furthermore, a hybrid-view adaptive
fusion module dynamically aggregates features along both channel and spatial
dimensions, enhancing the overall representation. Experiments are conducted on
an in-house dataset containing 590 subjects who underwent prostate biopsy.
Comparative and ablation results prove the efficacy of our method. The code is
available at https://github.com/mock1ngbrd/HVAN.
