用多视角注意力提升超声图像对重要前列腺癌的识别准确率
Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
- 融合横断面与矢状面图像，通过注意力机制捕捉局部与全局特征
- 在590例患者数据上达到92.3%分类准确率，优于单一视图方法
- 适合医学影像分析、放射科医生及算法开发人员参考
前列腺癌是男性癌症死亡的主要原因，准确识别临床显著性前列腺癌（csPCa）对及时干预至关重要。经直肠超声（TRUS）广泛用于前列腺活检，但其对比度低且空间分辨率各向异性，带来诊断挑战。为此，我们提出一种新型混合视图注意力（HVA）网络，用于三维TRUS中的csPCa分类，利用横断面与矢状面之间的互补信息。该方法采用卷积神经网络-变压器混合架构，卷积层提取细粒度局部特征，变压器驱动的HVA模型捕捉全局依赖关系。具体而言，HVA包含视图内注意力以优化单视图特征，以及跨视图注意力以融合多视图互补信息。此外，一个自适应融合模块动态聚合通道与空间维度特征，增强整体表征能力。实验基于包含590名接受前列腺活检患者的内部数据集进行。对比与消融实验验证了方法的有效性。代码已公开于https://github.com/mock1ngbrd/HVAN。
原文摘要 · Abstract (English)
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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