标题： 基于混合视图注意的TRUS中csPCa分类 显示英文标题

标题： Hybrid-View Attention for csPCa Classification in TRUS

摘要： 前列腺癌（PCa）是男性癌症相关死亡的主要原因，准确识别临床显著性PCa（csPCa）对于及时干预至关重要。 经直肠超声（TRUS）广泛用于前列腺活检；然而，其对比度低和各向异性空间分辨率带来了诊断挑战。 为解决这些限制，我们提出了一种新的混合视图注意力（HVA）网络，用于3D TRUS中的csPCa分类，该网络利用横断面和矢状面的互补信息。 我们的方法集成了CNN-Transformer混合架构，其中卷积层提取细粒度局部特征，基于Transformer的HVA模型捕捉全局依赖关系。 具体而言，HVA包括视图内注意力以细化单个视图内的特征，以及跨视图注意力以整合不同视图之间的互补信息。 此外，一种混合视图自适应融合模块在通道和空间维度上动态聚合特征，增强了整体表示。 实验在一个包含590名接受前列腺活检的受试者的内部数据集上进行。 比较和消融结果证明了我们方法的有效性。 代码可在 https://github.com/mock1ngbrd/HVAN 获取。 显示英文摘要

摘要： 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.