Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound

• URL: http://arxiv.org/abs/2507.03421v2
• Date: Thu, 10 Jul 2025 03:48:57 GMT
• Title: Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
• Authors: Zetian Feng, Juan Fu, Xuebin Zou, Hongsheng Ye, Hong Wu, Jianhua Zhou, Yi Wang,
• Abstract summary: We propose a novel hybrid-view attention network for csPCa classification in 3D TRUS.<n>Our approach integrates a CNN-transformer hybrid architecture, where convolutional layers extract fine-grained local features.<n>Experiments are conducted on an in-house dataset containing 590 subjects who underwent prostate biopsy.
• Score: 4.662744612095781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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