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

arXiv: https://arxiv.org/abs/2507.03421 · HTML: https://mlsanity.com/paper/2507.03421/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound](https://mlsanity.com/paper/2207.00496/) (2022)
- [Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound](https://mlsanity.com/paper/2406.14069/) (2024)
- [A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound](https://mlsanity.com/paper/2407.00678/) (2024)
- [Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancer](https://mlsanity.com/paper/2402.08987/) (2024)

## Built on by (most cited first, 1 of 1)

- [Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework](https://mlsanity.com/paper/2607.01648/) (2026)

## Abstract (arXiv)

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.