论文标题
Z-SSMNET：用于前列腺癌检测和诊断双参数MRI的Zonal-Aware自我监督网络网络
Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRI
论文作者
论文摘要
双聚体磁共振成像（BPMRI）已成为检测和诊断临床上显着前列腺癌（CSPCA）的关键方式。开发基于AI的系统来使用BPMRI识别CSPCA可以通过提高效率和成本效益来改变PCA管理。但是，使用卷积神经网络（CNN）的当前最新方法在学习面内和来自各向异性图像的三维空间信息方面受到限制。他们的性能还取决于大型，多样化和井井有条的BPMRI数据集的可用性。我们提出了一个Zonal-Aware的自我监督网络网络（Z-SSMNET），该网络（Z-SSMNET）适应地整合了多维（2D/2.5D/3D）的卷积，以在平衡的方式中学习密集的切片内信息和稀疏的斜切间信息。提出了一种自我监督的学习（SSL）技术，以使用大规模的未标记数据来预先培训我们的网络，以了解BPMRI的外观，纹理和结构语义。它旨在在训练阶段捕获薄板内部和切片间信息。此外，我们将网络限制为专注于区域解剖区域，以进一步提高CSPCA的检测和诊断能力。我们在包含10000+多中心和多扫描仪数据的PI-CAI数据集上进行了广泛的实验。我们的Z-SSMNET在病变水平检测（AP得分为0.633）和患者级诊断（AUROC分数为0.881）方面都表现出色，确保了PI-CAI挑战的开发阶段的最高位置，并保持了强劲的性能，并保持了0.690的AP得分，并且AP得分为0.690，而AUROC得分为0.909，并确定了排名第二的位置。
Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cancer (csPCa). Developing AI-based systems to identify csPCa using bpMRI can transform PCa management by improving efficiency and cost-effectiveness. However, current state-of-the-art methods using convolutional neural networks (CNNs) are limited in learning in-plane and three-dimensional spatial information from anisotropic images. Their performances also depend on the availability of large, diverse, and well-annotated bpMRI datasets. We propose a Zonal-aware Self-supervised Mesh Network (Z-SSMNet) that adaptively integrates multi-dimensional (2D/2.5D/3D) convolutions to learn dense intra-slice information and sparse inter-slice information of the anisotropic bpMRI in a balanced manner. A self-supervised learning (SSL) technique is proposed to pre-train our network using large-scale unlabeled data to learn the appearance, texture, and structure semantics of bpMRI. It aims to capture both intra-slice and inter-slice information during the pre-training stage. Furthermore, we constrained our network to focus on the zonal anatomical regions to further improve the detection and diagnosis capability of csPCa. We conducted extensive experiments on the PI-CAI dataset comprising 10000+ multi-center and multi-scanner data. Our Z-SSMNet excelled in both lesion-level detection (AP score of 0.633) and patient-level diagnosis (AUROC score of 0.881), securing the top position in the Open Development Phase of the PI-CAI challenge and maintained strong performance, achieving an AP score of 0.690 and an AUROC score of 0.909, and securing the second-place ranking in the Closed Testing Phase.
