Wedi'i Gadw mewn:
| Prif Awduron: | , , , , , , |
|---|---|
| Fformat: | Preprint |
| Cyhoeddwyd: |
2025
|
| Pynciau: | |
| Mynediad Ar-lein: | https://arxiv.org/abs/2507.03421 |
| Tagiau: |
Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
|
| _version_ | 1866916835330359296 |
|---|---|
| author | Feng, Zetian Fu, Juan Zou, Xuebin Ye, Hongsheng Wu, Hong Zhou, Jianhua Wang, Yi |
| author_facet | Feng, Zetian Fu, Juan Zou, Xuebin Ye, Hongsheng Wu, Hong Zhou, Jianhua Wang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound Feng, Zetian Fu, Juan Zou, Xuebin Ye, Hongsheng Wu, Hong Zhou, Jianhua Wang, Yi Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.03421 |